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Dear everyone,
I am trying to conduct molecular dynamics simulations of NaK alloys. However, the relevant literature is scarce and unclear about the interaction potential. Does anyone know how to describe the potential between the Na and K atoms for the alloy simulation, especially in Lammps?
Any suggestion or comment would be highly appreciated.
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Dear Huaqiang Liu , Thank you for your feedback. I haven't worked on this potential. However, I will try to give you suggestions regarding this if I find some insights about the implementation of this potential in LAMMPS.
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I downloaded a CIF file in CCDC that I would like to convert to a PDB.
However, when I open the CIF with VESTA I have that the hydrogen atoms in the methyl groups are doubled, giving two superposed reasonable positions for the methyl (see attached figure).
If I save the figure as a pdb in VESTA, I end up with a .pdb containing the C attached to 6 hydrogens.
How can I generate a PDB with just 3 hydrogens for each carbon? I'm not very concerned about which position of the methyl is going to be selected, however, I would like to have just one of them.
I would like either a solution with VESTA or using other free software.
Thanks!
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Otherwise you can just go into the cif file (open it in a text editor) and delete tose three ydrogens by hand.
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Whats the coordinate of atoms in a crystal with cubic structure that atomic positions for them are same, like 4a(0,0,0)?
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Perhaps, some idea about the answer of your question can be extracted from the preprint article given at ResearchGate link: https://www.researchgate.net/publication/352830671 or http://dx.doi.org/10.13140/RG.2.2.27720.65287
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I am working on analyzing change in electronic and band structural properties of two layers of Graphene when subjected to external constraints.
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Dear Akash Saxena and thank you very much for asking this interesting technical question. to construct a graphene 2D structure please use the CIF file in VISTA software it's free. you may also use a crystal maker for this also.
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As is know, for a pure element, the equilibrium vacancy concentration can be calculated with the vacancy formation energy and temperature:
Nv/N = exp(-Qv/k*T)
However, for a solid solution (A-B system), how to calculated the equilibrium vacancy concentration with the vacancy formation energy for various atoms (Qv_A and Qv_B )?
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To calculate the equilibrium vacancy concentration per atom you should use:
C=C0 exp-(Q/kT).
Since you have the enthalpy of formation Q in eV, this value is for 1 vacancy. Consequently you should use the Boltzmann-Constant k instead of R (R is for 1 mol; k is for 1 Atom: R=k x N)
C0 is not temperature dependent and is defined by the local change of entropy if 1 vacancy is formed. C0 should be taken from experimental literature, since it is not easy to calculate theoretically.
To calculate the vacancy concentration per atom is quite useful, because one gets a feeling for the relation between atoms and the un-occupied places (vacancies). To calculate the absolute number of vacancies, you shoud multiply your result per atom with the number of atoms per mol (Avogadro-Constant). bit easier because you do not need to think about k and R.
. If Q is in eV, then it is easier to convert the temperature to eV also. The rule is simple, 1eV = 11600 K. So 1000 K = 1000/11600 eV = 0.0862 eV, and Q/T = 0.9/0.0862 = 10.44.
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I'm trying to wannierize CdSe band by using Wien2wannier.
To do this, I need to select atoms and types of orbit for them as trial function.
These are called projection.
I don't know way to select appropriate projection.
How do you do this?
I tried some types of projection I thought were appropriate(by my poor knowledge) for wannierization and then calculated band dispersion using the wannierized orbit.
However, in any case, energies for every wave number ware 0.
Is this problem attributed to projection?
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x lapw1 x lapwso
x w2w will automatically decide the complex/real version for me? ‎If this is the case, I don't see the necessity to provide a "-c" option in the wien2wannier user guide for "x w2w". Physically, I think the eigenvectors generated from lapwso should be complex so that I tend to use "x w2w -c". Am I wrong here? I know the scripts like run_lapw for the self consistency can automatically decide real/complex for me. Sorry for being slow. Jianxin Sent from my BlackBerry 10 smartphone on the Verizon Wireless 4G LTE network. From: Oleg Rubel Sent: Friday, December 19, 2014 5:30 PM To: A Mailing list for WIEN2k users Reply To: A Mailing list for WIEN2k users Subject: Re: [Wien] initial projections for wien2wannier with spin orbital coupling The 'x' script should take care of the selection complex/real version for you. Oleg On Friday, December 19, 2014, Zhu, Jianxin <[email protected]<mailto:[email protected]>> wrote: Hi Elias, For the spin-orbit coupling (but non spin-poalrized) case, we usual have the following for the charge self-consistency run x lapw1 x lapwso x lapw2 -so -c In the wien2wannier90 user guide, I see there is an option of "-c" for w2w. In the above situation, should we use this option x w2w -c (It is equivalent to run w2wc case.def)? I tend to do so (by thinking the wave function can be complex) but I am not sure. In the case without inversion symmetry, since I have x lapw1 -c x lapwso -c x lapw2 -so -c for the charge self-consistency, I do use x w2w -c (after running x lapw1 -c, and x lapwso -c) for initial projection Amn. Thank you in advance for the explanation. Jianxin -- ################################# Jian-Xin Zhu, Ph.D Theoretical Division, MS B262 Los Alamos National Laboratory Los Alamos, New Mexico 87545 Phone: (505) 667 2363 (T-4); (505) 667 6602 (CINT) Fax: (505) 665 4063 Email (main): [email protected] Email (backup): [email protected] URL: http://theory.lanl.gov ################################# On 12/12/14 4:02 AM, "Elias Assmann" <[email protected]> wrote: >On 12/12/2014 07:34 AM, Zhu, Jianxin wrote: >> 1/ runsp_c_lapw –so is one way for spin-orbit coupling but >> non-spin-polarized case. > >runsp_c does a *spin-polarized* calculation where up and dn are >*constrained* to be equal. > >> Elias has also suggested the following way after faking the spin >> polarized calculation -- >> $ x w2w -so -up && x w2w -so -dn >> $ cp CASE.eig CASE.eigup >> $ cp CASE.eig CASE.eigdn >> $ x wannier90 -so > >I think you are referring to this message ><http://www.mail-archive.com/wien%40zeus.theochem.tuwien.ac.at/msg11327.ht >ml>. > This is just due to a minor bug in the ‘wannier90’ wrapper script. >Anyway, it applies to non-SP SO cases, and so should not be relevant here. > >> 2/ For the initial projection, the following order may be more >> consistent with the Bloch wavefunction in the spin space (spinor). > >Let me note that the order of orbitals is completely up to your >preference (it will make no difference to the calculation). > >> From: 李志 <[email protected] <mailto:[email protected]>> >> Firtsly, I do a spin polarization calculation with SOC, and zero >> local magnetic moment, i.e. runsp_c_lapw -so >> >> Then, preparew2w_dir wannier >> >> init_w2w -up >> >> However, 26 inital projections are requied, and we just 7+5+1=13 >> orbiatls basis. > >With SO, it is not possible to treat “up” and “dn” states separately. >You need to define 13*2=26 Wannier functions; Amn and Mmn will be >computed for the “up” and “dn” parts and then added, i.e. > >Amn = Amn(up) + Amn(dn) >Mmn = Mmn(up) + Mmn(dn) > >> So, I should type into f,d,and s-orbitals two times,i.e.: >> >> 1:f >> >> 1:f >> >> 1:d >> >> 1:d >> >> 1:s >> >> 1:s >> >> correct? > >That looks correct. For brevity, I would write it as > >1:s,p >1:s,p > > > Elias >_______________________________________________ >Wien mailing list >[email protected] >http://zeus.theochem.tuwien.ac.at/mailman/listinfo/wien >SEARCH the MAILING-LIST at: >http://www.mail-archive.com/[email protected]/index.html _______________________________________________ Wien mailing list [email protected] http://zeus.theochem.tuwien.ac.at/mailman/listinfo/wien SEARCH the MAILING-LIST at: http://www.mail-archive.com/[email protected]/index.html -- -- Oleg Rubel Scientist, Thunder Bay Regional Research Institute Adjunct Professor, Dept Physics, Lakehead University 290 Munro St, Thunder Bay, P7A 7T1, Ontario, Canada Phone: +1-807-7663350 Fax: +1-807-3441948 E-mail: [email protected]<mailto:[email protected]> Homepage: http://www.tbrri.com/~orubel/
_______________________________________________ Wien mailing list [email protected] http://zeus.theochem.tuwien.ac.at/mailman/listinfo/wien SEARCH the MAILING-LIST at: http://www.mail-archive.com/[email protected]/index.html
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  • [Wien] initial projections for wien2wannier with spin orbita... 李志
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I plan to use MD simulation to study a macromolecular system containing more than 300 million atoms after solvation. I am aware that large systems of up to a few million atoms have been successfully simulated at the atomistic level. But I haven't met many where up to 300 million particles have been studied. I will be grateful if you can recommend an MD code that can handle this. Thanks in anticipation.
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For sure NAMD had been used alongside CHARMM force field to run large scale simulations of cromatophores
This was several year ago, so it's likely that large systems can now be handled by most of MD software. The point is having the computational facilities and the code correctly compiled to properly scale on the hardware you have
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Hi all,
I was trying to run a simulation in NAMD but encountered the following error:
FATAL ERROR: CudaTileListKernel::buildTileLists, maximum shared memory allocation exceeded. Too many atoms in a patch
Any suggestion regarding the solution to this issue is highly appreciated.
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Has anyone found a solution for this problem? I only have 23K atoms and encountered this problem.
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I am trying to calculate the mean squared displacement and had written a code in fortran. I wrote this code using a reference book by Daan Frenkel and Berend Smit on Molecular Dynamics Simulation. I am having a hard time in getting the exact plot as proposed for a particular system. Can anybody have a look at it and suggest me where I am doing wrong in calculating the Mean Squared displacement as function of time.
Also, I would like to mention a little detail of the code. I used Origin shift method here in code where "i1" is for origin shift and calculating particles displacement over (total_time - 1) for all the particles present in a simulation cell. First, I am averaging over the number of data points (tau) accumulated (due to the shift of origin) for each atom for a particular origin (i1) in loop and finally after accumulating it over total number of particles (ntotal) and averaging it(ensemble_avg).
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In Triboelectrification, when we create friction between two materials(especially insulators) electrons or atoms can exchange between them. Normally it is very difficult to separate the charge or electrons from the insulators. How come simple friction can create exchange between two materials.?
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When two different materials are rubbed together, there is a transfer of electrons from one material to the other material. This causes one object to become positively charged (the electron loser) and the other object to become negatively charged (the electron gainer). 2.
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the protein i am working on is HPV16 E6 ID : 4xr8 chain H . It contains 2 zinc atoms that are important as they form zinc finger. i Manually edited pdb file in column 79,80 and add 2+ to both Zn atoms. However, after preparing protein pdbqt file using auto dock tools , message show up saying there are zero charge on theses atoms Zn Zn . I ignored the message but after selecting the binding site residues and
flexible residues >>> input>>> choose macromolecule then
flexible residues >>> choose torsions in currently selected residues
flexible residues >>>output>>> save flexible pdbqt
an error "Sorry, there are no Gasteiger parameters available for atom E6_CHAIN_H:H: ZN201:ZN"
appeared.
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Hi,
only add Zn ions after generating the .PDBQT file.
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Good day
I am using the Materials Studio Visualizer Atom Volumes & Surfaces tool to investigate crystalline porous materials. Although the "Solvent surfaces" are calculated fine, the "Connolly surface" erroneously includes spurious areas that are obviously not accessible to the probe - as shown in the attached example. Is there a reason for and/or a work around for this?
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Dewald van Heerden Hi, I also tried the same settings, But I do not see the same image as you posted. Is there any settings needed to be changed? I have attached the figure
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We are interested to prepare a setup for powder metallurgy studies. I'm trying to find a lab-scale machine or technology to produce good quality spherical metal powders of active metals like magnesium similar to what can be found in gas atomizers.
Any recommendation?
I've come across a new atomization technology by ATO lab and it should be for lab scale use but I haven't seen a published work yet using it? anyone had experience with it?
Thank you!
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yes I have an ATO+ atomizer.
To have more information contact me by email.
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I am using Orca 4. What besis set should I used? The molecule contain carbon, nitrogen and hydrogen atom.
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Hydrogen bonding is not a question of the basis set but of the functional/method.
In DFT you need dispersion correction which is not depending on the functional. If your system is small you should compare with correlated methods. Dispersion arises from the interaction of excited states which are not included in DFT.
First choose a method, then decide how large your basis set can be.
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Dear all,
I am currently facing the convergence problem of my GaCe surface calculation. The calculation is for a single Ce atom on a gallium o surface, but has severe convergence problems when the surface is doped with cerium, so the calculations do not converge and oscillate between E+0 and E-1 but do not converge. The use of a dimer of cerium also seems to have no effect on the convergence.
From my point of view, this is a problem with the parameters, which I have not been able to solve so far. Therefore i am reaching out to you to find some help. Can anyone spot my mistake?
INCAR:
SYSTEM = alphaGa_slab_PE
PREC = Normal
ENCUT = 350 eV
EDIFF = 1E-4
GGA = PE
LVDW = T
IVDW = 11
IDIPOL = 3
LDIPOL = F
_LVHAR = T
IALGO = 38
NSIM = 4
NELM = 100
NELMIN = 8
SYMPREC = 1E-8
ISPIN = 2
ICHARG = 2
MAGMOM = 144*0 2*1
ISMEAR = 0
SIGMA = 0.01
NSW = 100
IOPT = 1
IBRION = 3
POTIM = 0.
ISIF = 2
EDIFFG = -0.01
LCHARG = .FALSE.
LWAVE = .FALSE.
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  1. Relax the system to a converged geometry.
  2. Using the relaxed geometry, perform static calculation using the following tags: ...
  3. Sum the output charge files AECCAR0 AECCAR2 using the the chgsum.pl script. ...
  4. Run the Bader charge analysis to CHGCAR output from step#3.
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Hello everyone,
I used HDOCK to model the homodimeric form of a protein. I want to refine the docking solutions using the protocol briefly described in the paper below:
The authors used Amber for energy minimization and MD simulation employing the GB implicit solvent model. After energy minimization and heating steps, the authors did use restraints to avoid dissociation due to possible initial sterical overlap and to improve the sterical complementarity at the interface.
I want to know which tools from AmberTools I should use to apply the following restrictions in the production run:
  • small distance restraint (force constant 0.5 kcal/mol.Angstron²) between the center of mass of ligand and receptor [in my case, the two subunits of the homodimer];
  • weak distance restraints between the C-alpha atoms at the interface of the proteins to push the binding partners toward each other (force constant 0.25 kcal/mol.Angstron²).
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A) Distance restraint between the center of mass of the first subunit and the center of mass of the second subunit.
B) Positional restraint applied separately to the center of mass or geometrical center or initial coordinates of each subunit.
The magnitude of the force constant is system-dependent: it should be high enough to prevent dissociation but not unnecessarily too high.
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In the Entropic Information Theory, information is the code of the cosmos where
The code is what creates the process but is itself the process
This Process can be perceived as the Conscious Living Mind Matrix of the Universe(*) or as life as the storage of the information and the possibility to updated it by a process of self-learning to perpetuate that form of information
(*):― Max Planck : “All matter originates and exists only by virtue of a force which brings the particle of an atom to vibration and holds this most minute solar system of the atom together. We must assume behind this force the existence of a conscious and intelligent mind. This mind is the matrix of all matter.”
What is your point of view about this framework..?
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First a want to thank you for you active interest.. ;:)-
i will begin by respond to this first position of reasoning,...
""If consciousness is a coded process""
first What is consciousness ?
consciousness following Entropic Information Theory :
consciousness is information treated by subject,..
it need subject and object
not the information, because information can exist by itself
as it is code and code being itself the process
Now the code perspective,.....
Yet the human Brain CAN NOT be coded entirely !!!
because some human brain process can not be computabe as the process of creation or intuition
those are not computable
So Consciousness not the universe can not be coded
there is a fundamental distinction between "non alive code" and "alive code"
the self upated level in this definition of live,......
""life as the storage of the information and the possibility to updated it by a process of self-learning to perpetuate that form of information""
second point to respond from your question,...
""energy/information/mass limits and laws of physics""
you are on the way,... indeed,..
Here we experimentally show the existence of the Landauer bound in a generic model of a one-bit memory.
Using a system of a single colloidal particle trapped in a modulated double-well potential,
we establish that the mean dissipated heat saturates at the Landauer bound in the limit of long erasure cycles.
This result demonstrates the intimate link between information theory and thermodynamics.
It further highlights the ultimate physical limit of irreversible computation.
Bérut, A., Arakelyan, A., Petrosyan, A. et al. Experimental verification of Landauer’s principle linking information and thermodynamics. Nature 483, 187–189 (2012). https://doi.org/10.1038/nature10872
MOREOVER,....
If mass-energy-information equivalence principle is correct and information has indeed mass,
a digital informational universe would contain a lot of it, and perhaps the missing dark matter could be just information,”
"the number of bits of information that the visible universe would contain to make up all the missing dark matter.
It appears that my estimates of information bit content of the universe are very close to his estimates."
Whether information energy is the source of dark energy can be tested experimentally by searching
for evidence of a dark energy value w>-1.0 in the most recent period (z<<0.😎.
Landauer’s principle demonstrated that information is physical.
The mass- energy-information equivalence principle extrapolated this and demonstrated
that information has in fact mass.
Since there is a lot of information associated with the baryonic mass in the universe,
then it must be a huge amount of mass that corresponds to that information.
This is the basis of postulating that the information is the 5th element, or the 5th form of matter,”
The information catastrophe featured
AIP Advances 10, 085014 (2020); https://doi.org/10.1063/5.0019941
Melvin M. Vopsona)
Anna Soska
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GROMACS version: gmx, version 2018.1
GROMACS modification: No
Here post your question
Dear all, I am trying to simulate an revere micellar system, using GROMACS. I used CHARMM ff. During Mdrun, in NVT, the jobis getting terminated by following error:
Step 58549, time 58.549 (ps) LINCS WARNING
relative constraint deviation after LINCS:
rms 3.250535, max 436.089630 (between atoms 4457 and 4453)
bonds that rotated more than 30 degrees:
atom 1 atom 2 angle previous, current, constraint length
4454 4453 76.8 0.1339 1.6622 0.1337
4453 4458 99.5 0.1295 0.6384 0.1337
4458 4463 46.1 0.1495 0.3319 0.1483
4463 4464 57.4 0.1094 0.1917 0.1100
4463 4465 67.4 0.1097 0.2224 0.1100
4463 4466 62.1 0.1524 0.2952 0.1530
4466 4467 33.3 0.1102 0.1205 0.1100
4466 4468 37.8 0.1101 0.1303 0.1100
4466 4469 30.3 0.1533 0.1719 0.1530
4456 4452 90.1 0.1506 0.6091 0.1080
4452 4458 92.5 0.1394 0.6490 0.1370
4459 4454 83.9 0.1464 1.3864 0.1470
4452 4451 91.5 0.1358 1.8022 0.1370
4454 4451 85.3 0.1408 1.6710 0.1370
4455 4451 89.4 0.2011 1.9289 0.1080
4460 4459 114.7 0.1097 0.2535 0.1090
4461 4459 100.3 0.1097 0.3348 0.1090
4462 4459 86.9 0.1101 0.3523 0.1090
Wrote pdb files with previous and current coordinates
Wrote pdb files with previous and current coordinates
-------------------------------------------------------
Program: gmx mdrun, version 2018.1
Source file: src/gromacs/ewald/pme-redistribute.cpp (line 282)
MPI rank: 6 (out of 8)
Fatal error:
1 particles communicated to PME rank 6 are more than 2/3 times the cut-off out
of the domain decomposition cell of their charge group in dimension x.
This usually means that your system is not well equilibrated.
For more information and tips for troubleshooting, please check the GROMACS
-------------------------------------------------------
I minimized the system three times with different different forces (though the F max was E3, while I set it to 10). I have also used Berendsen thermostat during the NVT simulation. So, what may be the possible reasons and how can I solve it? As I read earlier replies, the primary reasons for such errors are mentioned to the geometry problem. I uploaded the parameter file here.
Thanks
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Have you visualized the trajectory and checked the behavior of atoms ID 4457 and 4453?
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I have a 20a x 17a x 20a simulation box filled with bcc tungsten atoms. What lammps command can I write to make 20a x 2.5a x 20a at the bottom of the box "immobile"?
The script below shows how my atoms were created:
# ---------- Create Atoms --------------------------
## define crystal structure and lattice constant a_0
## define direction vectors, i.e., set x=[100], y=[010], z=[001] and origin point.
variable a_0 equal 3.1648
lattice bcc 3.1648 orient x 1 0 0 orient y 0 1 0 orient z 0 0 1 &
origin 0.1 0.1 0.1
region box block 0 20 0 17 0 20 ## define box sizes along x, y, z (in the unit of a_0)
create_box 1 box ## create the simulation box, allowing a max of one specie
create_atoms 1 box ## create type-1 metal atoms in the box
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Hi Emmanuel,
The method I used when I needed to do something similar to you was the following:
- define the atoms that you want to keep immobile as a group
- set the initial velocities of the atoms in that group to zero
- then set the forces acting on the atoms in that group to zero
With the initial velocities being set to zero and no forces acting on them, the atoms in that group won't move.
The line I used to set the forces to zero was
fix freezebottom bottomlayer setforce 0.0 0.0 0.0
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Hello all,
I am trying to create a periodic box in python representing Zr and then create a function that is able to tell you what is the charge density at some selected point, given I already have a function for the charge density for a single atom.
I am pretty new to python and I have looked into ASE, but I am wondering if there is an easier way to do this.
Thank you for your help!
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i am follow the answer
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Hi, I use the molecular dynamic simulation for refining dimer protein structure. But whenever I run it, I see each structure separate from each other and be outside a simulation box. Why does this issue happens even if I have applied position restraint on all heavy atoms?
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I think that is enough.
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When solving the phase problem in x-ray crystallography using the heavy atom isomorphic replacement method, we cannot calculate the patterson function for the protein structure factor and instead calculate it for the heavy atom structure factor. However, why is it that we can calculate the patterson function for the protein structure factor without problems in the molecular replacement method?
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Dear Jane, as a synthetic chemist I'm not an expert enough to give you a qualified answer to your interesting technical question. However, I just came across the following potentially useful article which might help you in your analysis:
An introduction to molecular replacement
Fortunately this article has been posted by the authors as public full text on RG. Thus you can freely download the pdf file.
Good luck with your research and best wishes!
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I have a system of atoms. I want to create atoms with initial Maxwellian velocities. How can I generate such velocities using Python?
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How does LAMMPS identify the atomic size of atoms, does this information exist in potential file?
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Dipankar Roy Thank you
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Hello,
I have been trying to run a protein-ligand simulation. I am trying to get through the energy minimization step of the processes, the program gives me the following:
Fatal error:
There are inconsistent shifts over periodic boundaries in a molecule type
consisting of 32 atoms. The longest distance involved in such interactions is
8.463 nm which is above half the box length. Either you have excessively large
distances between atoms in bonded interactions or your system is exploding
I understand that I should somehow decrease the distance, but I am not sure which file would allow me to do this. I am wondering if maybe the em.mdp file is the problem. I used the sample file on the website:
; LINES STARTING WITH ';' ARE COMMENTS
title = Minimization ; Title of run
; Parameters describing what to do, when to stop and what to save
integrator = steep ; Algorithm (steep = steepest descent minimization)
emtol = 1000.0 ; Stop minimization when the maximum force < 10.0 kJ/mol
emstep = 0.01 ; Energy step size
nsteps = 50000 ; Maximum number of (minimization) steps to perform
; Parameters describing how to find the neighbors of each atom and how to calculate the interactions
nstlist = 1 ; Frequency to update the neighbor list and long range forces
cutoff-scheme = Verlet
ns_type = grid ; Method to determine neighbor list (simple, grid)
rlist = 1.2 ; Cut-off for making neighbor list (short range forces)
coulombtype = PME ; Treatment of long range electrostatic interactions
rcoulomb = 1.2 ; long range electrostatic cut-off
vdwtype = cutoff
vdw-modifier = force-switch
rvdw-switch = 1.0
rvdw = 1.2 ; long range Van der Waals cut-off
pbc = xyz ; Periodic Boundary Conditions
DispCorr = no
Will someone please advise how I can fix this issue?
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Please check if you are using the same force field or if there are any other dissimilarities. Please go through the following link; it might help you out.
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Dear all,
I am trying to calculate the energy of H2 molecules using the RPBE method, whereas the calculation result shows that the hydrogen atom will push itself against the other one, and finally become two isolated hydrogen atoms.
I am wondering what should be corrected for my input files as attached? I really appreciate any suggestions or comments.
Thanks,
  • INCAR
SYSTEM = H2
ISTART = 0
ICHARG = 2
PREC = Accurate
LREAL = .FALSE.
ISIF = 2
# Electronic relaxation
ENCUT = 400
ALGO = Fast
# Ionic relxation
IBRION = 2
EDIFFG = -0.01
EDIFF = 1E-5
NSW = 300
# DOS related parameters
ISMEAR = 0
SIGMA = 0.01
# Write wavefunctions
LWAVE = .FALSE.
LCHARG = .FALSE.
# Spin polarizations
ISPIN = 2
# Dipole corrections
IDIPOL = 4
# RPBE functional
GGA = RE
LUSE_VDW = .TRUE.
AGGAC = 0.0000
# Parallelization
NPAR = 4
LPLANE = .TRUE.
  • KPOINTS
Gamma-point only
1 ! one k-point
Rec ! in units of the reciprocal lattice vector
0 0 0 1 ! 3 coordinates and weight
  • POSCAR
H
1.0
20.639999954088 0.000000000000 0.000000000000
0.000000000000 20.000000000000 0.000000000000
0.000000000000 0.000000000000 20.000000000000
H
2
Selective dynamics
Direct
0.482038536955 0.500000000000 0.500000000000 F F F
0.517961463045 0.500000000000 0.500000000000 T T T
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while you were doing the calculation with ISPIN=2 for H2 molecule, NUPDOWN=2 were also set. It means the difference between the number of electrons in H2 system was 2. It is basically impossible .
You have got two following options:
1) Try setting ISPIN=1 instead of ISPIN=2.
2) Using ISPIN=2 with NUPDOWN=0. (If you need the spin-polarisation)
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Hi all,
How to calculate bond energy between two atoms using DFT calculations in material studio? Do you have any idea how to do that? Which module should I use for this?
Thanks in advance!
Regards,
Sharmi
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Hadi Jabbar Alagealy : why should she use Hartree-Fock? A well-chosen functional+basis set give more accurate results and, depending on the choice, the DFT calculation may run faster?
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I have a 50nm thin Cu film. How do I calculate the deposition rate in Atoms/s instead of A/s. Do I just normalize the thickness by the atomic diameter?
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It would depend on the crystal structure of the film. Copper is FCC in most cases so I would divide thickness by lattice constant/sqrt(2) to convert to number of atoms. Using atomic diameter over lattice parameters leaves more room for confusion since there are various definitions of the former depending on the nature of bonding in the material.
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Hello
I got XRD data of Al-LLZO
I wanted to calculate its density, so I needed to know
how many atoms are in an unit cell but I couldn't find out
is there any way can I find it form this data? or if you know another way
please let me know
thanks
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Hi.
you can't tell directly from a powdered diffraction analysis. From XRD it is possible to verify that this is really a cubic phase with a structure corresponding to La3Li7Zr2O12, with space group of Ia-3d. Dopant is not important right now. In the cif file of this compound (COD ID 7206766) it is stated that the number of formula units in the unit cell is 8 (ie no. of atom is 8*formula). If the doped material is isotypal, just include it in the mole mass calculation, calculate the cell volume from the XRD and update the density.
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May anyone have a good reference about the aromatic substitution of trifluoromethyl group? which hydrogen atom substituted by -CF3 directly. Thank you in advance.
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Dear Syadza, sorry to see that your very interesting technical question has not been answered yet. Many years ago we did a lot of work involving 1,3,5-tris(trifluoromethyl)benzene. This was made by converting the –COOH groups of 1,3,5-benzene-tricarboxlic acid into –CF3 groups by means of SF4. However, work with SF4 requires especially equipped laboratories. For the direct trifluoromethyl substitution of aromatic rings please have a look e.g. atcthe following useful article:
Trifluoromethylation of arenes and heteroarenes by means of photoredox catalysis
(see attached pdf file)
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Can anyone explain when do we use isodesmic reactions to find HOF of unknown compounds and when do we use atomization method ? it is very confusing. Mostly detonation products are often difficult to design using isodesmic reaction. in some places, ppl have used atomization method to do the same. can anyone provide some clarity or reference of some paper?
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Heat of formation using isodesmic reaction and atomization reaction? when to use what?
in The systems G2, G2(MP2), and G2(MP2,SVP) total energies to heats of formation via atomization and formation reaction procedures is evaluated.
Both approaches give similar results, the former is shown to perform somewhat better, especially for organic molecules. The recommended general procedure for obtaining heats of formation at 0 K from G2-type total energies is to use G2-type atomization energies in conjunction with experimental gas-phase heats of formation for the constituent atoms. The corrections required to give heats of formation at finite temperatures can be obtained using scaled theoretically derived vibrational frequencies for the species under consideration together with temperature correction terms for the constituent elements based on experimental data. While the harmonic oscillator model is a good approximation for most vibrational modes, internal rotations with relatively low frequencies are more accurately treated within the free rotor approximation.
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Hi!
I have a protein system where a single water molecule can play a role in a ligand stabilization. To check whether this single water molecule is important in binding, I wanted to perform TI (thermodynamics integration) calculations, starting from system with water and annihilate it. My plan was to use AMBER software (PMEMD) for this purpose. I would replace water molecule with dummy atoms during TI. After many trials I finally got my tleap output (parmtop & prmcrd), but while running the script I am getting an error.
My question is: am I doing it right or there is a simpler way than TI to calculate an impact on energy after cutting our water molecule from the system?
I will be grateful for any tips!
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  • The total amount of energy released when a positron and an electron annihilate is 1.022 MeV, corresponding to the combined rest mass energies of the positron and electron. The energy is released in the form of photons.
  • The amount of energy (E) produced by annihilation is equal to the mass (m) that disappears multiplied by the square of the speed of light in a vacuum (c)—i.e., E = mc2.
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Hello everyone, I am looking for structures of clusters, so how can i do this? there are a program help us to propose and design a structures of cluster isomers? and find the coordinates of its atoms?
thank you in advents.
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Your very welcome. Stay Happy Stay Healthy.
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Because, I need the parameters of the Buckingham potential between two similar or different atoms in the simulations of phosphate-based glasses by molecular dynamics, for example, between rare-earth atoms like Er-Er, Y-Y, ...
If there is someone to help me find or calculate these parameters of the Buckingham potential.
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The GULP code (http://gulp.curtin.edu.au/gulp/), which is free for academic researchers, implements several strategies for fitting the parameters of interatomic potentials. You can use a known crystal structure, physical properties or vibrational frequencies, or even a DFT-determined potential energy surface, and then find the Buckingham parameters that give you the best match with your target observables.
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I would like to start a discussion on the challenges and limitations of spray coating small particles in a fluid bed dryer using the Wurster method.
The coating thickness is limited by the droplet size of the coating material. Droplets down to 10um can be generated with atomization. I'm curious if there is any advice out there on coating <100um particles?
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I recommend to start the discussion with the recent published article by Rongyi Zhang et.tal., titled "Novel Technique for Coating of Fine Particles Using Fluidized Bed and Aerosol Atomizer". It contains vital investigation for the process of small particles spray coating in a fluid bed dryer using the Wurster method. The study contributes to the development of a new coating process parameters for fine particles with diameters of around 50 μm (<100μm particles) without severe agglomeration in a Wurster fluidized bed.
Best regards...
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In order to perform the doping in Wien2k, we create a super cell of some dimensions according to the doping concentration. The atom/s at particular positions in the super cell are replaced by the dopants. However super structures are associated with Quasi random structures. How to perform the calculations? The literature and the data available so far is limited.
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Yun-Peng Wang Thank You Prof., for the answer, but if we get stuck anywhere, youtube and blogs of Professors around the world are full of tutorials, however dealing with Qusirandom structures does not seem to have such explanations till now. ATAT is a bit complex whereas the Supercell package seems user-friendly, but again, no tutorials are available. Most of the works on supercell structures have not considered SQS, that is also another reason it got less attention in the DFT calculations.
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Recently, I try to use the Shengbte software to calculate the thermal conductivity of SiC. However, I got very wrong results comparing to the results from references. I try to change all the settings, however, I am still unable to get the right results. When I am trying, I found a strange phenomenon when I use thirdorder_vasp.py to generate the third-order force constant configurations. The final number of the configurations is related to the format of your POSCAR. At the first, I use a POSCAR which is not in a stander format, the stander here means some atoms are in the position of "0 0 0". The thirdorder_vasp.py (V.1.1.1) gives me 172 configurations, and I got the wrong results in the Shengbte calculation. Then I use the "phonopy --symmetry" to convert the POSCAR into a stander format, the thirdorder_vasp.py gives me 216 configurations, and got the right results then. When I change to an old version thirdorder_vasp.py (v.1.1.0), both the POSCARS mentioned above give me the same number of configurations. So the lastest thirdorder_vasp.py has a bug when your POSCAR is not in stander format.
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FourPhonon is a computational package that can calculate four-phonon scattering rates in crystals. It is built within ShengBTE framework, which is a well-recognized lattice thermal conductivity solver based on Boltzmann transport equation. An adaptive energy broadening scheme is implemented for the calculation of four-phonon scattering rates. In analogy with thirdorder.py in ShengBTE, we also provide a separate python script, Fourthorder.py, to calculate fourth-order interatomic force-constants. The extension module preserves all the nice features of the well-recognized lattice thermal conductivity solver ShengBTE, including good parallelism and straightforward workflow. In this paper, we discuss the general theory, program design, and example calculations on Si, BAs and LiCoO2.
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The correlation function for a two-level atom in contact with a reservoir of photons at temperature T is (I'm using natural units):
C_R(\tau)\propto PolyGamma[4, 1-i (\tau/\beta)],
where \beta=1/T.
A plot for T=300K shows that the reservoir decay time is \tau_R~0.25*10^{-13}s, whereas for T=10K, \tau_R~1.5*10^{-12}s. Both times are much less that the decay of an atom t_s~10^{-8}s, therefore, it looks like the Markov approximation works for low-temperatures. Nevertheless, in the literature, I find that such approximation fails when temperature decreases.
What is wrong with my logic?
Thanks.
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The quantum thermal machines often relies on master equations.It widely used, but often criticized and local approach for sub-systems locally coupled to thermal baths. The thermal baths couple to degrees of freedom . There are conceptually different approaches used in situations out of thermal equilibrium. Under consider thermodynamically relevant observables, such as heat currents, as well as the quantum state . In particular, for weak intersystem coupling, the local approach agrees with the exact solution, whereas the global approach fails for non-equilibrium situations.
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Hi all!
I have performed the MD simulation on aqueous lysozyme solution(withou external electric field) with GROMACS. Now, I want to map the spatial distribution of internal electrostatic force field.
I tried to calculate the electrostatic potential at given position(rRef) by summing up the Coulomb potentials generated by all charged atoms(N) in the simulation box, as the following equation. Obviously, only atioms in the original box were taken into account here. However, in MD simulation, the PME method was used for electrostatic force, which means not only atoms in simulation box but also in the mirror boxes were considered. So, I wonder whether this method will cause notabe mistakes?
Is there better way to tackle this problem?Thanks in advance for your advice.
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Hi Kang,
My opinion is that the basic features of important interactions that may produce many complex patterns of scientific interests are already catched with a short-ranged calculation.
I am not saying that ignoring the long-range part is correct. Lots of papers have say that long-range electrostatics are important.... However, the long-range part will not make significant difference. Specifically in your illustrative picture, such a interaction pattern could be easily fitted with some prior models, such as exp(-kr) with r being the distance. Just having several points below a threshold, e.g., 10 Angstroms, we can already fit the whole curve. There could be many redundant and not useful information with significant statistical fluctuations in the distant positions.
Best,
Zhaoxi
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What is the reason for the poor performance of the nitrogen-doped porous carbon-supported cobalt single atom material in ORR, OER, and HER?
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Dear Ming-Jun, many thanks for asking this very interesting technical question. Of course it is very difficult to judge as an outsider what possibly went wrong in your case. My personal suggestion would be to discuss this problem in detailwith your PhD supervisor. Moreover, you can try to reproduce a procedure described in the literature and see if you get the same results. Unfortunately, research articles in this field do not report negative results and do not explain what could perhaps go wrong. When you have a look at relevant articles on this topic you only read about "Efficient.." or "Highly efficient...". For example, please have a closer look at the following potentially useful articles:
Cobalt single atoms anchored on nitrogen-doped porous carbon as an efficient catalyst for oxidation of silanes
High efficiency nitrogen doping and single atom cobalt anchoring via supermolecules for oxygen reduction electrocatalysis
Co, N co-doped porous carbons as high-performance oxygen reduction electrocatalysts
Single-Atom Catalysts for Electrochemical Hydrogen Evolution Reaction: Recent Advances and Future Perspectives
The latter article is freely available as public full text on RG.
Good luck with your work!
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I know that magnons are quasi-particles which are the misaligned spins in an atom at low temperatures. These are also called as spin waves.Could anyone please explain me, the above, in a much detailed manner? Also what exactly do you mean by one- magnon or two-magnon scattering?
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For a description of the scattering of magnons at the elementary level please see the following classical reference (last 2 chapters):
The Nature Of Magnetism (Series: Science for Everyone) by Profs. M. I. Kaganov
& V. M. Tsukernik, Mir publishers, 1985.
Best Regards.
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I want to study the effect of dopant in the 2D oxide material using vasp, can anyone guide how to calculate the dos of atoms around the dopant.
regards
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I think you might have got your answer. You can use the VASPKIT code (https://vaspkit.com) to post-process your data. Or extract the DOS from the respective file manually for every single atom.
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Hello everyone,
I am trying to do a scan by using ghost atoms placed in the center of mass of two monomere. The thing is it seems that Gaussian want to define those ghost atom with a basis set but cannot use the one ask in the input.
# opt=modredundant m062x/6-31+g(d,p) nosymm scrf=(smd,solvent=chloroform) empiricaldispersion=gd3 geom=connectivity
Here is my input :
Title
0 1
C -1.42209300 2.85152800 -0.07551900
C -0.74016400 1.63745300 -0.13704100
C 0.65395900 1.64969700 -0.13777000
C 1.31626500 2.87414000 -0.07914900
C 0.63547300 4.09789800 -0.03425300
C -0.76204000 4.08675000 -0.02957400
C 1.67512100 0.64674800 -0.17270900
C 1.68676800 -0.74595000 -0.22797600
C 2.91210700 -1.40890700 -0.22987500
C 4.13683000 -0.72900300 -0.19724900
C 4.12620500 0.66753600 -0.14266600
C 2.89022300 1.32768000 -0.12638400
C -0.71198900 -1.77749600 -0.26830700
C -1.37420700 -3.00327000 -0.28902400
C -0.69353600 -4.22760100 -0.31967000
C 0.70399800 -4.21679400 -0.31225000
C 1.36399800 -2.98103500 -0.28029400
C 0.68210500 -1.76546100 -0.26510500
C -4.19496200 0.59935300 -0.15591100
C -4.18420000 -0.79763200 -0.19869200
C -2.94820200 -1.45731500 -0.22908100
C -1.73315300 -0.77476200 -0.22870000
C -1.74489400 0.61811900 -0.17791600
C -2.97026100 1.27982800 -0.13778900
O 2.68236800 2.68928600 -0.07313000
O 2.72675800 -2.77445800 -0.26392600
O -2.78489600 2.64448000 -0.07822400
O -2.74031200 -2.81937200 -0.26928100
C -0.10748700 3.07263700 3.24448200
C 0.01750700 1.68536000 3.21641200
C 1.29407200 1.12550500 3.19689800
C 2.40048000 1.97312300 3.20441400
C 2.28143000 3.36884100 3.24397600
C 1.00130700 3.92925200 3.26694200
C 1.81434200 -0.20796200 3.15531400
C 1.25435700 -1.48376300 3.11065800
C 2.10177800 -2.58907600 3.05696300
C 3.49801700 -2.46995100 3.05961700
C 4.05878900 -1.19056100 3.10691300
C 3.20188400 -0.08250900 3.14498900
C -1.35630800 -1.44379700 3.11053900
C -2.46273100 -2.29017000 3.06407900
C -2.34368600 -3.68554200 3.01636600
C -1.06350000 -4.24643000 3.00502200
C 0.04523700 -3.39013700 3.03577600
C -0.07975900 -2.00375000 3.09497700
C -3.56021800 2.15306000 3.20130400
C -4.12106800 0.87336500 3.16627000
C -3.26410900 -0.23496500 3.13620200
C -1.87659700 -0.11050600 3.15642500
C -1.31656000 1.16545500 3.19688200
C -2.16400100 2.27190300 3.21128500
O 3.57091700 1.24539100 3.17621700
O 1.37349300 -3.75873600 3.01368600
O -1.43571500 3.44189300 3.24324500
O -3.63316700 -1.56214600 3.08210300
H 5.13555200 -1.05981200 3.10144900
H 4.13139900 -3.34946200 3.01987100
H -0.93318600 -5.32233700 2.96081800
H -3.22415000 -4.31800700 2.98355600
H -1.23792500 -5.16566800 -0.33366200
H 1.26332800 -5.14590600 -0.32226600
H 3.16183900 4.00225000 3.24837200
H 0.87086500 5.00587500 3.28762400
H 1.17981900 5.03509300 0.00893500
H -1.32139700 5.01480100 0.01548600
H 5.05546500 1.22546700 -0.10570400
H 5.07476900 -1.27377900 -0.20046200
H -5.11333900 -1.35700000 -0.20078100
H -5.13306800 1.14286100 -0.12337200
H -5.19779700 0.74344500 3.14895000
H -4.19341300 3.03355900 3.21284900
Bq -0.03111800 -0.15914600 3.15371850
Bq -0.02901600 -0.06400700 -0.20070450
1 2 1.5 6 1.5 27 1.0
2 3 1.5 23 1.5
3 4 1.5 7 1.5
4 5 1.5 25 1.0
5 6 1.5 65 1.0
6 66 1.0
7 8 1.5 12 1.5
8 9 1.5 18 1.5
9 10 1.5 26 1.0
10 11 1.5 68 1.0
11 12 1.5 67 1.0
12 25 1.0
13 14 1.5 18 1.5 22 1.5
14 15 1.5 28 1.0
15 16 1.5 61 1.0
16 17 1.5 62 1.0
17 18 1.5 26 1.0
18
19 20 1.5 24 1.5 70 1.0
20 21 1.5 69 1.0
21 22 1.5 28 1.0
22 23 1.5
23 24 1.5
24 27 1.0
25
26
27
28
29 30 1.5 34 1.5 55 1.0
30 31 1.5 51 1.5
31 32 1.5 35 1.5
32 33 1.5 53 1.0
33 34 1.5 63 1.0
34 64 1.0
35 36 1.5 40 1.5
36 37 1.5 46 1.5
37 38 1.5 54 1.0
38 39 1.5 58 1.0
39 40 1.5 57 1.0
40 53 1.0
41 42 1.5 46 1.5 50 1.5
42 43 1.5 56 1.0
43 44 1.5 60 1.0
44 45 1.5 59 1.0
45 46 1.5 54 1.0
46
47 48 1.5 52 1.5 72 1.0
48 49 1.5 71 1.0
49 50 1.5 56 1.0
50 51 1.5
51 52 1.5
52 55 1.0
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
D 53 73 74 25 S 1 24.161900
A 31 73 74 F
A 74 73 41 F
A 73 74 22 F
A 73 74 7 F
And the error at the end of the log file :
Warning: center 73 has no basis functions!
Warning: center 74 has no basis functions!
1088 basis functions, 1904 primitive gaussians, 1144 cartesian basis functions
184 alpha electrons 184 beta electrons
nuclear repulsion energy 8244.5763318142 Hartrees.
NAtoms= 74 NActive= 74 NUniq= 74 SFac= 1.00D+00 NAtFMM= 60 NAOKFM=T Big=T
Integral buffers will be 131072 words long.
Raffenetti 2 integral format.
Two-electron integral symmetry is turned off.
R6DQ: No Q value available for IA= 0
Error termination via Lnk1e in /opt/software/gaussian/g16.b01/l301.exe at Thu Jun 10 14:52:03 2021.
Anyone know what to do to solve that problem?
Sincerely,
Mathieu
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A warning appears for the use of ghost atoms, but it seems that trouble is Grimme's D3 parameter. You can add function to describe dispersion effects with final version of Gaussian
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Dear Researchers,
I am using Chimera 1.15 on PC as well as on Laptop. Both are having Windows10. On PC, the mouse hovering function is not working. When I hover the mouse over the hydrogen bond or bonded protein or any atom, it should show the description. It works fine in laptop but not working on PC.
Though, internet search on forum suggests some settings but its not working.
Can anybody suggest the solution?
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If they both are of same configuration, will work! Else you will need to check for bugs, PC load, and may be after your clean-up of PC reinstall the software. It is compatible for both the systems.
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Since atom size plays one of the important roles while performing Molecular Dynamic Simulations. Does anybody know what should be the required RAM of the processor based on the atom size of the protein-ligand complex in Gromacs?
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Dear Thakur
When doing MD stimulation, 16-32 GB of RAM will enough, even if the machine is of a lesser grade. Clock Speed - 3.7 (Min) operate smoothly, and Minimum i3/ryzen 3 processor advises due to the heat created when operation. But what's really significant for MD simulation is having GPU that will speed the total process and even you may switch your task from CPU to GPU. Desmond, accessible with Schrodinger's Maestro, only supports GPU.
regards
Marzan
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Hello everyone
I am trying to investigate the ground state of a series of middle sized molecules containing an iodine atom (using g16 package).
Based on your experience in Computational Chemistry, would you mind telling me what is the most effective basis set (BS) up to now when it comes to describing the iodine atom.
For more clarity, I will be combining two basis sets, one BS to account for the iodine atom and the other for the rest of atoms in the system (C,O,N,H). The problem is now in the choice of the BS that better describes the core and valence shells of iodine.
What do you think about the LANL2DZ ECP basis set?
Looking forward to reading you.
Regards,
Bienfait
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For those who may be interested, this is a selection of publications where the choice of BS was dictated by the agreement with structural (XRD) or spectroscopic (FT-Raman) experimental features:
1. Riccardo Montis, Massimiliano Arca, M. Carla Aragoni, Alexander J. Blake, Carlo Castellano, Francesco Demartin, Francesco Isaia, Vito Lippolis, Anna Pintus, Eder J. Lenardão, Gelson Perin, Alice E. O’Connor, Samuel Thurow, “Structural diversity in the products formed by the reactions of 2-arylselanyl pyridine derivatives and dihalogens”, New J. Chem., 42, 10592–10602 (2018).
2. Riccardo Montis, Massimiliano Arca, M. Carla Aragoni, Antonio Bauzá, Francesco Demartin, Antonio Frontera, Francesco Isaia, Vito Lippolis, “Hydrogen- and halogen-bond cooperativity in determining the crystal packing of dihalogen charge-transfer adducts: a study case from heterocyclic pentatomic chalcogenone donors”, CrystEngComm, 19, 4401–4412 (2017).
3. Gianluca Ciancaleoni, Massimiliano Arca, Giovanni F. Caramori, Gernot Frenking, Felipe S. S. Schneider, Vito Lippolis, “Bonding Analysis of Homo- and Hetero-Trihalides Species”, Eur. J. Inorg. Chem., 3804–3812 (2016).
4. Annalisa Mancini, M. Carla Aragoni, Ann L. Bingham, Carlo Castellano, Susanne L. Coles (née Huth), Francesco Demartin, Michael B. Hursthouse, Francesco Isaia, Vito Lippolis, Giuseppe Maninchedda, Anna Pintus, Massimiliano Arca, “Reactivity of fluoro-substituted bis(thiocarbonyl) donors with diiodine: An XRD, FT–Raman, and DFT Investigation”, Chem. – Asian J., 8, 3071-3078 (2013).
5. Annalisa Mancini, M. Carla Aragoni, Neil Bricklebank, Carlo Castellano, Francesco Demartin, Vito Lippolis, Francesco Isaia, Massimiliano Arca, “Formation of T-shaped vs. charge-transfer molecular adducts in the reactions between bis(thiocarbonyl) donors and Br2 and I2”, Chem. – Asian J., 8, 639-647 (2013).
6. Annalisa Mancini, Luca Pala, M. Carla Aragoni, Massimiliano Arca, Francesco A. Devillanova, Michael B. Hursthouse, Mark E. Light, Peter J. Skabara, Neil Bricklebank, “Structural and DFT studies of dibromine and diiodine adducts of a Sulfur-rich thiocarbonyl donor”, Eur. J. Inorg. Chem., 2373-2380 (2012).
7. M. Carla Aragoni, Massimiliano Arca, Francesco A. Devillanova, Michael B. Hursthouse, Susanne L. Huth, Francesco Isaia, Vito Lippolis, Annalisa Mancini, Gaetano Verani, “Reactions of halogens/interhalogens with polypyridyl substrates: the case of 2,4,6-tris-2-pyridyl-1,3,5-triazine”, Eur. J. Inorg. Chem., 3921-3928 (2008).
8. M. Carla Aragoni, Massimiliano Arca, Francesco Demartin, Francesco A. Devillanova, Alessandra Garau, Francesco Isaia, Vito Lippolis, Gaetano Verani, “DFT calculations, structural, and spectroscopic studies on the products formed between IBr and N,N'-dimethylbenzoimidazole-2(3H)-thione and -2(3H)-selone”, Dalton Trans., 2252-2258 (2005).
9. M. Carla Aragoni, Massimiliano Arca, Francesco A. Devillanova, Michael B. Hursthouse, Susanne L. Huth, Francesco Isaia, Vito Lippolis, Annalisa Mancini, Helen R. Ogilvie, Gaetano Verani, “Reactions of pyridyl donors with halogens and interhalogens: an X-ray diffraction and FT-Raman investigation”, J. Organomet. Chem., 690, 1923-1934 (2004).
10. M. Carla Aragoni, Massimiliano Arca, Francesco A. Devillanova, Michael B. Hursthouse, Susanne L. Huth, Francesco Isaia, Vito Lippolis, Annalisa Mancini, “Square-pyramidal bonding of I2 molecules at the I– nodes of a polyiodide infinite pseudo-cubic 3D-network”, CrystEngComm, 6(87), 540-542 (2004).
11. M. Carla Aragoni, Massimiliano Arca, Francesco A. Devillanova, Francesco Isaia, Vito Lippolis, Annalisa Mancini, Luca Pala, Alexandra M. Z. Slawin, J. Derek Woollins, “First example of infinite polybromide 2D network”, Chem. Commun., 2226-2227 (2003).
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Note: Seeking complete tutorials and/or guidance on performing Molecular Docking and Molecular Dynamics (MD) simulation using the following software.
Softwares: PyRx, Pymol, Molecular Operating Environment (MOE), Schrodinger Suites, server-based softwares, etc.
Introduction: In the field of molecular modeling, docking is a method that predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Knowledge of the preferred orientation, in turn, may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions. The associations between biologically relevant molecules such as proteins, peptides, nucleic acids, carbohydrates, and lipids play a central role in signal transduction. Furthermore, the relative orientation of the two interacting partners may affect the type of signal produced (e.g., agonism vs. antagonism). Therefore, docking is useful for predicting both the strength and type of signal produced. Molecular docking is one of the most frequently used methods in structure-based drug design due to its ability to predict the binding conformation of small-molecule ligands to the appropriate target binding site. Characterization of the binding behavior plays an important role in the rational design of drugs and elucidating fundamental biochemical processes.
Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanics force fields.
Therefore, it would be great to have guidance from the experts in these areas on how to perform Molecular Docking and Molecular Dynamics simulation.
Thank you very much for your consideration.
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That generally does not happen. Because the workflow is pretty much straightforward. Do you have the trial version or full version of the software?
If you have the full version, contact customer care, they will help you out using a remote connection.
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I wanna do calculation of KI, KBr, KCl into water and compare these dissosiation energy (ex:KI→K+I ΔHr)
Normally, we chose wB97XD/6-311++(d,p), but in this case, 6-311 doesn't comprehensive to I atom.
What basis should I select for calculations.
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Dear Florent Louis,
Thank you for your information.
What should be the basis set for optimization of a compound having N, O and I atoms? Please comment.
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I am running an ISIF=2 calculation on VASP for an amourphous system with about 130 atoms with following INCAR details
ENCUT =500
EDIFF = 1.0E-05
EDIFFG = 1.0E-04
IBRION=2
Algo=VeryFast
NSW=99
ISIF = 2
ISMEAR = -1
As can be seen in the attached screenshot, dE value (-0.11819E+01) in the last electronic step is more than EDIFF (1.0E-5) I had set in the INCAR. Same applies for the EDIFFG tag (converged at 33rd IONIC step) which is also not satisfied. Yet, the calculation is shown to converge.
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Hi Shoeb Athar , very strange... Can you send me the full INCAR, POSCAR, and OUTCAR?
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As the output from LAMMPS are generally based Total free energies, free stresses of atoms, stresses in certain directions. How can we use these data to calculate parameters like elastic modulus or other properties of materials. Are there any standard examples for atomistic to continuum (atc) package in LAMMPS. Can you please suggest relevant articles.
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There is a folder after extracting LAMMPS package, named atc in lib folder lammps*/lib/atc. It says that:
ATC (Atom To Continuum methods)
Reese Jones, Jeremy Templeton, Jonathan Zimmerman (Sandia National Labs)
rjones, jatempl, jzimmer at sandia.gov
September 2009
This is version 1.0 of the ATC library, which provides continuum field
estimation and molecular dynamics-finite element coupling methods.
-------------------------------------------------
This directory has source files to build a library that LAMMPS
links against when using the USER-ATC package.
This library must be built with a C++ compiler, before LAMMPS is
built, so LAMMPS can link against it.
You can type "make lib-atc" from the src directory to see help on how
to build this library via make commands, or you can do the same thing
by typing "python Install.py" from within this directory, or you can
do it manually by following the instructions below.
Build the library using one of the provided Makefile.* files or create
your own, specific to your compiler and system. For example:
make -f Makefile.g++
Note that the ATC library makes MPI calls, so you must build it with
the same MPI library that is used to build LAMMPS, i.e. as specified
by settings in the lammps/src/MAKE/Makefile.machine file you are
using.
When you are done building this library, two files should
exist in this directory:
libatc.a the library LAMMPS will link against
Makefile.lammps settings the LAMMPS Makefile will import
Makefile.lammps is created by the make command, by copying one of the
Makefile.lammps.* files. See the EXTRAMAKE setting at the top of the
Makefile.* files.
IMPORTANT: You must examine the final Makefile.lammps to insure it is
correct for your system, else the LAMMPS build will likely fail.
Makefile.lammps has settings for 3 variables:
user-atc_SYSINC = leave blank for this package
user-atc_SYSLIB = BLAS and LAPACK libraries needed by this package
user-atc_SYSPATH = path(s) to where those libraries are
You have 3 choices for these settings:
a) If the 2 libraries are already installed on your system, the
settings in Makefile.lammps.installed should work.
b) If they are not, you can install them yourself, and specify the
appropriate settings accordingly in a Makefile.lammps.* file
and set the EXTRAMAKE setting in Makefile.* to that file.
c) Use the minimalist version of these libraries provided with LAMMPS
in lib/linalg, by using Makefile.lammps.linalg. In this case you also
need to build the library in lib/linalg; see the lib/linalg/README
file for more details.
So, the answer is yes, but not for magnetic properties.
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Dear all,
I have some dimers including monomers each contains 60 to 90 atoms. Doing my desired calculations, I first need to prepare structures in their optimum geometry. Also it is needed to find the optimum distances between monomers. For small and medium-sized systems, l do rigid scan to find the optimum distance between two monomers then I do a geometry optimization on the structure located in the potential well. The real minimum point is examined by a frequency calculation. What is the procedure for a such big system like my dimers? The same method can handle the process? Is simulated annealing a suitable method in this case?
Best regards
Bahareh Bamdad
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Check the following paper ! if you need more information ask me, good Luck.
The IMOMO method: Integration of different levels of molecular orbital approximations for geometry optimization of large systems: Test for n-butane conformation and SN2 reaction: RCl+Cl−
Humbel, Stéphane, Sieber, Stefan, Morokuma, Keiji
Journal:
The Journal of Chemical Physics
Year:
1996
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I have done md simulation using following commands-
what is the meaning of this warning, it it harmful, hI attached e warning image and nvt.mdp in which I have done few changes
gmx
Add Amber99SB-ILDN forcefield
gmx pdb2gmx -ff amber99sb-ildn -f target.pdb -o DNA.pdb -p topol.top -water spce -ignh
Add hydrogen to Ligand.pdb using Pymol
antechamber -i Ligand.pdb -o Ligand.mol2 -fi pdb -fo mol2 -c gas
copy acpype.py from acpype_lib in your folder
python acpype.py -i Ligand.mol2
# Merge DNA.pdb + updated Ligand_NEW.pdb -> Complex.pdb
grep -h ATOM DNA.pdb Ligand.acpype/Ligand_NEW.pdb >| Complex.pdb
cp Ligand.acpype/Ligand_GMX.itp Ligand.itp
# `#include "Ligand.itp"` has to be inserted right after
# `#include "amber99sb.ff/forcefield.itp"`
# line and before `Protein_*.itp` line in _Complex.top_
cat Complex.top | sed '/forcefield\.itp\"/a\
#include "Ligand.itp"
echo "Ligand 1" >> Complex2.top
#newbox is formed
gmx editconf -bt triclinic -f Complex.pdb -o Complex.pdb -d 1.5
#solvate this box
gmx solvate -cp Complex.pdb -cs spc216.gro -o Complex_b4ion.pdb -p Complex.top
#download ions.mdp from tutorial of protein ligand complex of Gromacs
gmx grompp -f ions.mdp -c Complex_b4ion.pdb -p Complex.top -o Complex_b4ion.tpr
cp Complex.top Complex_ion.top
echo 6| gmx genion -s Complex_b4ion.tpr -o Complex_b4em.pdb -neutral -conc 0.15 -p Complex_ion.top
#grp no. is acc to SOL
Select a continuous group of solvent molecules
Group 0 ( System) has 15639 elements
Group 1 ( DNA) has 478 elements
Group 2 ( UNK) has 59 elements
Group 3 ( Other) has 59 elements
Group 4 ( UNK) has 59 elements
Group 5 ( Water) has 15102 elements
Group 6 ( SOL) has 15102 elements
Group 7 ( non-Water) has 537 elements
Select a group: Selected 6: 'SOL'
Number of (3-atomic) solvent molecules: 5034
mv Complex_ion.top Complex.top
#download em.mdp from tutorial of protein ligand complex of Gromacs
gmx grompp -f em.mdp -c Complex_b4em.pdb -p Complex.top -o em.tpr
gmx mdrun -v -deffnm em
#Restraining the Ligand
gmx make_ndx -f Ligand.pdb -o index_Ligand.ndx
> 0 & ! a H*
> q
gmx genrestr -f Ligand.pdb -n index_Ligand.ndx -o posre_Ligand.itp -fc 1000 1000 1000
Select group to position restrain
Group 0 ( System) has 59 elements
Group 1 ( Other) has 59 elements
Group 2 ( UNK) has 59 elements
Group 3 ( System_&_!H*) has 25 elements
Select a group: 3
Selected 3: 'System_&_!H*'
#include posre_Ligand.itp in topology file i.e. Complex.top in this manner
; Include Position restraint file
#ifdef POSRES
#include "posre.itp"
#endif
; Include ligand topology
#include "Ligand.itp"
; Ligand position restraints
#ifdef POSRES
#include "posre_Ligand.itp"
#endif
; Include water topology
#include "./charmm36-mar2019.ff/tip3p.itp"
gmx make_ndx -f em.gro -o index.ndx
0 System : 15551 atoms
1 DNA : 478 atoms
2 UNK : 59 atoms
3 NA : 29 atoms
4 CL : 15 atoms
5 Other : 59 atoms
6 UNK : 59 atoms
7 NA : 29 atoms
8 CL : 15 atoms
9 Water : 14970 atoms
10 SOL : 14970 atoms
11 non-Water : 581 atoms
12 Ion : 44 atoms
13 UNK : 59 atoms
14 NA : 29 atoms
15 CL : 15 atoms
16 Water_and_ions : 15014 atoms
nr : group '!': not 'name' nr name 'splitch' nr Enter: list groups
'a': atom '&': and 'del' nr 'splitres' nr 'l': list residues
't': atom type '|': or 'keep' nr 'splitat' nr 'h': help
'r': residue 'res' nr 'chain' char
"name": group 'case': case sensitive 'q': save and quit
'ri': residue index
Merge the "DNA" and "UNK" groups with the following, where ">" indicates the make_ndx prompt:
> 1|2
Copied index group 1 'DNA'
Copied index group 2 'UNK'
Merged two groups with OR: 478 59 -> 537
17 DNA_UNK : 537 atoms
> q
#We can now set tc-grps = DNA_UNK Water_and_ions in nvt.mdp, npt.mdp and md.mdp
#download nvt.mdp from tutorial of protein ligand complex of Gromacs
gmx grompp -f nvt.mdp -c em.gro -r em.gro -p Complex.top -n index.ndx -o nvt.tpr
gmx mdrun -deffnm nvt
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you should check the itp and gro file of you DNA and ligand, this problem may be attributed to the unreseanable structure of your DNA or ligands
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Dear all,
The complex I am trying to simulate has protein ( 10 chains), DNA, and RNA I ran the simulation for 2 us so far but the biological event I want to observe requires a very long MD simulation. I decided to accelerate the sampling using the AWH method. I am not familiar enough with the AWH method or other methods such as umbrella sampling. I have watched the two webinars on AWH but I am still not sure about the mdp options. Attached is the mdp I used and the error I got!
I believed that I need to choose a reference atom for pulling, But on what basis?
pull = yes ; The reaction coordinate (RC) is defined using pull coordinates. pull-ngroups = 12 ; The number of atom groups needed to define the pull coordinate. pull-ncoords = 7 ; Number of pull coordinates. pull-nstxout = 1000 ; Step interval to output the coordinate values to the pullx.xvg. pull-nstfout = 0 ; Step interval to output the applied force (skip here).
pull-group1-name = Protein_chain_A ; Name of pull group 1 corresponding to an entry in an index file. pull-group2-name = Protein_chain_B ; Same, but for group 2. pull-group3-name = Protein_chain_C pull-group4-name = Protein_chain_D pull-group5-name = Protein_chain_E pull-group6-name = Protein_chain_F pull-group7-name = Protein_chain_G pull-group8-name = Protein_chain_H pull-group9-name = Protein_chain_I pull-group10-name = Protein_chain_J pull-group11-name = RNA pull-group12-name = DNA
pull-group1-pbcatom = 0 pull-group2-pbcatom = 0 pull-group3-pbcatom = 0 pull-group4-pbcatom = 0 pull-group5-pbcatom = 0 pull-group6-pbcatom = 0 pull-group7-pbcatom = 0 pull-group8-pbcatom = 0 pull-group9-pbcatom = 0 pull-group10-pbcatom = 0 pull-group11-pbcatom = 0 pull-group12-pbcatom = 0
pull-coord1-groups = 1 2 ; Which groups define coordinate 1? Here, groups 1 and 2. pull-coord2-groups = 3 4 pull-coord3-groups = 5 6 pull-coord4-groups = 7 8 pull-coord5-groups = 9 10 pull-coord6-groups = 11 12 pull-coord7-groups = 11 12
pull-coord1-geometry = distance ; How is the coordinate defined? Here by the COM distance. pull-coord1-type = external-potential ; Apply the bias using an external module. pull-coord1-potential-provider = AWH ; The external module is called AWH!
awh = yes ; AWH on. awh-nstout = 50000 ; Step interval for writing awh*.xvg files. awh-nbias = 1 ; One bias, could have multiple. awh1-ndim = 1 ; Dimensionality of the RC, each dimension per pull coordinate. pull-coord1-groups awh1-dim1-coord-index = 1 ; Map RC dimension to pull coordinate index (here 1–>1) awh1-dim1-start = 0.25 ; Sampling interval min value (nm) awh1-dim1-end = 0.70 ; Sampling interval max value (nm) awh1-dim1-force-constant = 128000 ; Force constant of the harmonic potential (kJ/(mol*nm^2)) awh1-dim1-diffusion = 5e-5 ; Estimate of the diffusion (nm^2/ps),used to initial update size, how quezly the system moves awh1-error-init = 5 ; Estimate of the error of diffusion , used to set initial update size awh-share-multisim = yes ; Share bias across simulations awh1-share-group = 1 ; Non-zero share group index
ERROR 12 [file AWH3.mdp]: When the maximum distance from a pull group reference atom to other atoms in the group is larger than 0.5 times half the box size a centrally placed atom should be chosen as pbcatom. Pull group 12 is larger than that and does not have a specific atom selected as reference atom.
Any advice you could give would be much appreciated
Thank you so much!
Amnah
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AWH calculates the free energy along an order parameter of the system. Free energy barriers are overcome by adaptively tuning a bias potential along the order parameter such that the biased distribution along the parameter converges toward a chosen target distribution. The fundamental equation governing the tuning is: log(target) = bias - free energy, where the bias and free energy are initially unknown. Typically the target distribution is simply chosen uniform, such that the bias completely flattens the free energy landscape.
Regards,
Shafagat
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It is a one-dimensional ultrathin nanowire of gold atoms. I think the figure shows it's partly a covalent and ionic bond, which is not supposed to be.
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Why would you expect bonds between atoms of the same type to be non-covalent? Each atom contributes approximately the same electron density to the space in between the atoms. Of course, the situation will be different at the ends of the chain.
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benzene C-C bond length is 1.39 angstrom, graphene 1.42 angstrom, so like an inscribed circle in a perfect hexagon, anything narrower than 1.20 or 1.229 angstrom diameter would suppose to pass through it. H, He, as well as most 2nd and 3rd row post-transition elements at periodic table have radius (ionic, covalent, atomic).But atoms are not solid, and the upper and lower (or front and back) delocalized electron cloud would act as potential barrier (not necessarily purely coulombic). However in quantum mechanical realm, especially lighter elements can more easily quantum-tunnel. Have there been any comprehensive review on selective atomic/molecular species sieving with PAH/ Graphene sheets? please let me know
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Dear Sumit Bhowmick many thanks for asking this very interesting and surprising technical question. There are a number of relevant articles in the chemical literature in which the selective atomic/molecular ieving with graphene sheets is reported. For example, please have a look at the following interesting articles:
Selective Molecular Sieving through Porous Graphene
This paper has been posted as public full text on RG.
Graphene: An impermeable or selectively permeable membrane for atomic species?
Graphene-Based Membranes for Molecular Separation
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This is the error message :
ERROR 1 [file md_pull.mdp]: When the maximum distance from a pull group reference atom to other atoms in the group is larger than 0.5 times half the box size a centrally placed atom should be chosen as pbcatom. Pull group 1 is larger than that and does not have a specific atom selected as reference atom.
Pull group natoms pbc atom distance at start reference at t=0
1 9518 4759
2 2993 11015 4.661 nm 4.661 nm
Estimate for the relative computational load of the PME mesh part: 0.18This run will generate roughly 1924 Mb of datawriting run input file...
I have also attached the mdp file.
I am following Justin Lemkul's Umbrella Sampling tutorial.
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This means that group 1 is very large and some atoms in that group are distant from each other more than 0.5 times the box size. This is a problem for GROMACS when it tries to calculate distances, since PBC are on and the true distance is not well defined anymore.
Try to visualize group 1 with some tool like VMD and try to find an atom that is ca. at the centre of your group 1. Take that atom number from the .gro/.pdb/your structure file and then add the option
pull-group1-pbcatom = #number_of_your_central_atom
When you do not specify this option, gromacs just searches for the central atom in the group from the list, i.e., if you have a group of 10 atoms gromacs uses the fifth, which does not correspond necessarily with the geometrical centre of your group. This usually is not a problem, but when groups are larger than half the box then the choice could be not ideal to calculate distances with PBC, and so you have to specify a better atom for gromacs to use.
Hope this helps,
Nicola
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Starting from the bulk gold and going down to atomically precise metal cluster, we wanted to know how does the energy of excitation for 2p3/2 --> 5d transition will change with particle size
We have a range of atomically precise clusters say Au4, Au7, Au11, Au25 Au102, and Au nanoparticles of ~2 nm.
Thanking you
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Mr./Dr. Sharma, You need to first check Google and the RG also, before you put the query!
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I am working with an intel core i7 in a dell computer. I work typically in systems ranging from 40 atoms to 140 atoms in a 5*5*5 mesh. These are WO3 monoclinic lattices to superlattices. I need a way of calculating the estimated operation time on Medea VASP GUI. Can somebody please help me with some ideas?
I do not understand if increasing the no. of atoms increases the computation time, how so really. How are they actually depending on each other, Can anyone suggest some relations form of equations or general theory.
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This is a difficult thing to do, but very valuable nonetheless. However, I don't recommend you start with Hartree-Fock at all, that's a performance nightmare in a plane-wave basis like VASP unless you work very hard.
I recommend you start with a nice semi-local functional like LDA or PBE, and something easy like bulk silicon or aluminium, and run some benchmarks to try to estimate the calculation time per SCF cycle. To help you get started:
* k-points
Almost all the computational bottlenecks will scale linearly with k-points (the symmetrised set, not the unfolded set)
* Plane-waves
Most operations will scale linearly with plane-wave basis set size, which itself scales as Ecut^1.5 (the FFT scales as N log N, for N plane-waves, though the prefactor depends on the radix); it also scales with the simulation cell volume
* Bands
Band-scaling is more complicated. Many operations are linear in the number of bands, but orthonormalisation and diagonalisation have steps which scale as bands^3 and steps which scale as plane-waves * bands^2
There's also the application of the non-local pseudopotentials, which scale with the number of plane-waves * projectors * bands (in reciprocal-space; if you don't care about forces you might get away with applying it in real-space). Depending on exactly how VASP works, there might be steps which scale as projectors^2 or even projectors^3 as well.
There aren't many parts which scale with the number of atoms directly (forces, phonons), but changing the number of atoms will change the number of bands and projectors. You might also want to change the simulation cell size to accommodate your atoms which would change the basis-set size, and you'd probably also change the k-points to be consistent.
Finally, what I've mentioned above is the scaling for the computational bottlenecks with large simulations. For smaller simulations, there are lots of other operations which may take up significant time -- and this is all in serial, in parallel there are communication costs as well.
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Hello;
I want to do molecular docking with the FTO protein. PDB identity of this protein: 3LFM. While doing the docking process, I will delete the inhibitors named 3-methylthymidine and N-OXALYLGLYCINE, and prepare the pdbqt file, where I will do the hydrogen addition and the charge addition. My question is: Apart from these two chemical residues, there is also an iron atom group in the structure. Should I delete this iron atom as well?
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First of all find out the binding site (pocket) for your specific protein and see that whether the metal ion is included in it or not. If it is included in the binding site than you have to include this ion for molecular docking. Second is, you can add hydrogens and charges to your proteins from Autodock tools Also you have to include your ion parameter manually as mentioned on Autodock site or else the error will be shown. I hope it works for you.
All the best!
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I am learning NAMD for simulating membrane embedded proteins. While running simulation, can we use different versions of Charmm parameter files?
Also, an error stating "FATAL ERROR: DIDN'T FIND vdW PARAMETER FOR ATOM TYPE ON3", is coming, but there are no ON3 atoms in my structure. For running this simulation, I have used parameter files as mentioned below:
parameters par_all36_lipid.prm
parameters par_all36m_prot.prm
parameters toppar_water_ions.str
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Thanks for the suggestion @Imon Mandal. I have checked both pdb and psf file and atom ON3 is in neither.
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I am recently interested in physical vapour deposition processes of Boron on semiconductors/insulators.
The books tell me much about sputtering rate, sputter yield, vapour pressure, etc.
However, I could not find a statistics about the number of atoms in vapours or clusters evaporated.
I believe that the number of atoms in a cluster will depend on i) nature of the elements. simple metals like Al will behave differently from element like C, B, Se; ii) temperature; etc.
Thanks for your kind help in advance.
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That depends very much on your PVD method and the parameters within that. In the gas phase you can have B atoms and clusters in all kinds of shapes and sizes, see e.g.:
The group of Lai-Sheng Wang has a lot of papers on anionic Boron clusters. They have been actually trying to deposit planar Boron clusters on surfaces to make "Borophene", but I am not up to date whether they succeeded.
However, when clusters impinge on a surface, their shape does not necessarily survive. I don't have a Boron example for that, but here is what happens to Gold clusters:
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Hello,
I am looking for software, which is possible to run from the command line and would not require protein-specific laborious setups, since I want to automate running it on a lot of structures.
I am interested in any software that would calculate atom-wise features from PDB files, such as energy terms or something similar but the key point is that the features consider also non-bonded interactions.
which generates partial charge and radius values for each atom.
Do you know anything that goes a little bit further and calculates terms that consider all nearby interactions for every atom?
If possible I would rather avoid setting up MD simulations using AMBER/Rosetta/GROMACS, however, if you know a pipeline that would be possible to execute on many protein structures at once, I would also appreciate it.
Thanks,
Jan
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This server gives you the atom-wise interaction details but not any energy terms. please check it.
Also please have look at foldX suite (http://foldxsuite.crg.eu/node/196)
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Hello everyone. I have two input files for reactant and product states of the same material. The atoms are to be arranged in the same order for both the states. However, the order of atoms in the product is different from the reactant in my case. I am using chemcraft to modify the inputs. How to rearrange the atoms of the product in the same order of the reactant?
Thank you in advance.
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Open both reactant and product structures in two different windows of ChemCraft software. Since you want to rearrange the coordinates of the product with respect to reactant coordinates, go to the ChemCraft window where you open the product structure and do the following.
1) Click on the Tools menu
2) Click on Structure comparer
3) Click on Update atomic sequence for best match with the structure from another ChemCraft window.
Please try this. I hope it helps!
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how can we determine total magnetization and magnetization of each atom in system for example Cu-doped ZnO. Please Help me in this regard.
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The total magnetisation is always written to the output file.
The Mulliken or Hirshfeld population analyses will compute atomic spin populations, with Hirshfeld generally preferred as being more rigorously defined. For Mulliken analysis I recommend a recent version of CASTEP, because there was a bug in earlier versions when computing contributions from p, d and f-states with symmetry. You can also use third-party tools, such as Bader analysis with the code from the Henkelman group.
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Hi Researchers,
I'm currently trying to generate parameters for the ligand and heme group that bind/crystallised with the protein structure. I need to know, which software or programme is the best to generate parameter for the heme group that has an iron (Fe) atom in it and to prepare them for molecular dynamic simulation using amber software.
I did try some software but that software didn't accept Fe atom. So, please suggest software that can compatible with the amber force field and accept Fe atom.
this is the type of error I got when I tried to generate parameter using playmolecule software !
Thank you in advance.
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which amber tool you used in your work. if you using the amber 19 or lesser version. then you need to download the script and give the python path in your script.
Carefully read the instructions and name of the atoms.
Best Regards
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For stability, the atom should fulfill the rule of octet, but in graphene the carbon has one of the 2p electron is unbonded, then how it become so stable? Please explain deeply. Thank you
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Dear Brajesh Kumar many thanks for your interesting and fundamental technical question. In addition to the helpful answers provided by Yuri Mirgorod and Pankaj Singh Rawat please have a look at the following relevant article:
The Potential Power of Graphene
This paper can bee freely downloaded as public full text from the internet. It contains a detailed explanation ofcthe bonding situation in graphene. You could also check articles detailing the bonding in graphite. After all, graphene represents single graphite layers.
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Index[127] 128 is larger than the number of atoms in the
trajectory file (127). There is a mismatch in the contents
of your -f, -s and/or -n files
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Dear Ravi
There is no details in your question, may be i
don't understand that. So, I think as Nicola Piasentin said :- Can you please post more info? What are you trying to do? Are you analyzing your trajectory?
I hoe you will find the solution for that.
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I have ACF .dat file
mostly mentioned in videos or answer that z - num_frozen_core - Bader population = Bader net atomic charge
how to find num_frozen core i have attached my files can any one help to clear this thanks in advance
i have to caluate the bader charge of Na atom ?
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This is an instructive video about calculating the Bader charge from VASP by Rasoul Khaledialidusti:
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i'm trying to run autodock, this series of warnings and an error popped
any ideas
C:/modelado_molecular/autogrid4.exe: WARNING: Found an H-bonding atom with three bonded atoms, atom serial number 5325
C:/modelado_molecular/autogrid4.exe: WARNING: Found an H-bonding atom with three bonded atoms, atom serial number 5326
C:/modelado_molecular/autogrid4.exe: WARNING: Unrecognized keyword in grid parameter file.
C:/modelado_molecular/autogrid4.exe: WARNING: Unrecognized keyword in grid parameter file.
C:/modelado_molecular/autogrid4.exe: WARNING: Unrecognized keyword in grid parameter file.
C:/modelado_molecular/autogrid4.exe: WARNING: Unrecognized keyword in grid parameter file.
C:/modelado_molecular/autogrid4.exe: ERROR: You need to set the number of grid points using "npts" before setting the ligand atom types, using "ligand_types".
i'm not sure what can i do
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First, i would visualize the structure, check atoms 5325 and 5326, see what the problem is, and correct the structure.
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I am using Lammps software and even though I have increased the temperature and also given the command to delete more than one atoms by defining the region still every time only one atom is getting deleted and I am not able to find the reason behind it. My main query is how can I create vacancies of different concentrations?
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You can use Atomsk software to delete atoms to create vacancy .
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Recently I synthesized a material. To specify, I loaded about 0.2%Mg on the synthesized MnO2 nanoparticles, the catalytic performance is very good. I guess that the trace amount of Mg may exist as the single-atom state on the surfaces of MnO2, but I don't know how to chatacterize the Mg. Usually in the publiactions, "heavy" metals such as Pt, Au or Ir acting as single atom catalysts will deposite on "light" supporting materials such as carbon, MOF, Al2O3 or TiO2. And STEM-HAADF will always be used to characterize these single atoms because it can detect and distinguish different elements with big difference in atomic numbers. However, in my research, the single atom Mg (I guess) is lighter than Mn, so STEM-HAADF does not work here. Can anyone give suggestions here? Thanks
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Thanks for your suggestions.
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There are different theories of graphene growth by chemical vapour deposition (CVD) techniques (e.g., microwave-CVD, plasma-CVD, thermal-CVD etc.) on a catalytic transition-metal substrate (commonly copper/nickel) is proposed by researchers.
Kindly put your expert insight whether there are any particular established mathematical relations/equations that follows carbon atoms diffusion in catalytic substrate or nucleation in surface to predict mono-layer/few-layers of graphene synthesis (considering the catalytic substrate is devoid of possible microstructural defects).
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Dear Samit Karmakar, here is the article, it may help you to understand the mechanism of graphene growth
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For Example. we have supercell of 2*2*2 ZnO (wurtzite structure), containing 32 atoms, 16 atoms are Zn and 16 atoms are O. Now two Zn atoms are replaced by Mn atoms in the supercell. How we set input files for AFM coupling and FM coupling calculations. what are important flags for FM and AFM.
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