Recent Questions - Matter Modeling Stack Exchange most recent 30 from mattermodeling.stackexchange.com 2020-10-22T06:32:14Z https://mattermodeling.stackexchange.com/feeds https://creativecommons.org/licenses/by-sa/4.0/rdf https://mattermodeling.stackexchange.com/q/3571 4 Spin orbit coupling from spin polarized density of states? Hitanshu Sachania https://mattermodeling.stackexchange.com/users/116 2020-10-21T13:57:37Z 2020-10-21T21:55:58Z <p>My knowledge in these areas of physics is lacking, so please expect naivety ahead (perhaps, dumb down your answer accordingly).</p> <p>From what I read online, spin orbit coupling is how the angular momentum of an electron w.r.t. the nucleus interacts with its spin. The word 'relativistic' came up every time. My quantum-dot sized brain already heats up at the mention of 'quantum mechanics' 🙈. What is spin orbit coupling (soc) and what does soc strength mean?</p> <p>I was told:</p> <p><span class="math-container">\begin{equation} soc\:strength = DoS_{(spin-up)_{E_F}} - DoS_{(spin-down)_{E_F}} \end{equation}</span> where <span class="math-container">$E_F$</span> = Fermi energy.</p> <p>Me: <span class="math-container">$(^@ \_ ^@)$</span></p> <p>Questions on this SE (<a href="https://mattermodeling.stackexchange.com/questions/308/how-to-incorporate-the-effect-of-spin-orbit-coupling-in-electronic-structure-cal">here</a> and <a href="https://mattermodeling.stackexchange.com/questions/1144/spin-orbit-interaction-with-dft">here</a>) suggest soc calculations are an entirely different class of calculations that build upon a spin polarised calculation.</p> <p>Me: <span class="math-container">$(^? \_ ^?)$</span></p> https://mattermodeling.stackexchange.com/q/3568 6 Find the value of density of states at the Fermi energy using VASP Hitanshu Sachania https://mattermodeling.stackexchange.com/users/116 2020-10-21T10:49:43Z 2020-10-21T12:17:26Z <p><code>VASP</code> has a tag <a href="https://www.vasp.at/wiki/index.php/NEDOS" rel="noreferrer">NEDOS</a>, which helps specify the # of points on which the DoS is evaluated. Other than that, we have <a href="https://www.vasp.at/wiki/index.php/EMIN" rel="noreferrer">EMIN</a> and <a href="https://www.vasp.at/wiki/index.php/EMAX" rel="noreferrer">EMAX</a>, which help decide the range of energy in which DoS is evaluated at NEDOS evenly spaced grid-points. This procedure misses the exact fermi energy point, at least in all of my calculations. Is there a way to make the code explicitly calculate DoS at the Fermi energy? I wish to avoid interpolation due to certain concerns as relayed <a href="https://mattermodeling.stackexchange.com/questions/3567/validity-of-interpolation-for-density-of-states">here</a>.</p> https://mattermodeling.stackexchange.com/q/3567 4 Validity of interpolation for density of states? Hitanshu Sachania https://mattermodeling.stackexchange.com/users/116 2020-10-21T10:36:30Z 2020-10-21T10:53:44Z <p>A density of states (DoS) curve seems like an example of a curve for which curve fitting doesn't seem to make sense, or does it? How could we interpolate on such a curve, given that we got the curve from a discrete dataset?</p> <p>I thought of using an integrated DoS curve instead, since it has a nature suitable for fitting. Any suggestions are welcome.</p> https://mattermodeling.stackexchange.com/q/3565 10 Does One-to-One Correspondence of Hohenberg Kohn Theorem Mean Bijective or Injective and How to Prove it? GalliumBeryllium https://mattermodeling.stackexchange.com/users/1398 2020-10-21T00:06:33Z 2020-10-21T06:27:33Z <p>I have asked a similar <a href="https://mattermodeling.stackexchange.com/q/2424/88">question</a> but after thinking about it I have a more specific question.</p> <p>According to <a href="https://books.google.co.in/books/about/Time_Dependent_Density_Functional_Theory.html?id=hCNNsC4sEtkC&amp;redir_esc=y" rel="nofollow noreferrer">Ullrich, Carsten A.. Time-Dependent Density-Functional Theory : Concepts and Applications</a>, the Hohenberg–Kohn theorem states</p> <blockquote> <p>In a finite, interacting N-electron system with a given particle–particle interaction there exists a one-to-one correspondence between the external potential <span class="math-container">$V(r)$</span> and the ground-state density <span class="math-container">$n_0(r)$</span>. In other words, the external potential is a unique functional of the ground-state density, <span class="math-container">$V[n_0](r)$</span>, up to an arbitrary additive constant.</p> </blockquote> <p>The way I understand it, assuming V differs by more than a constant and psi differs by more than a phase, the logic is: one potential (V) yields one hamiltonian (H) which yields a wave function (Ψ) which yields a density (n). V -&gt; Ψ -&gt; n.</p> <p>V -&gt; Ψ (ignoring constant) This is proven in HK theorem via proof by contradiction</p> <p>Ψ -&gt; n (ignoring phase factor) This is proven in HK theorem via proof by contradiction.</p> <p>Then they conclude that: We have thus shown that <span class="math-container">$Ψ_0$</span> and <span class="math-container">$Ψ′_0$</span> give different densities <span class="math-container">$n_0$</span> and <span class="math-container">$n′_0$</span>; but in the first step we showed that <span class="math-container">$Ψ_0$</span> and <span class="math-container">$Ψ′_0$</span> also come from different potentials <span class="math-container">$V$</span> and <span class="math-container">$V′$</span>. Therefore, a unique one-to-one correspondence exists between potentials and ground-state densities, which can be formally expressed by writing <span class="math-container">$V[n_0](r)$</span>, and thus <span class="math-container">$V[n_0]$</span>.</p> <p>This confuses me because they have only proven &quot;one direction.&quot; They have proven that two V's cannot give the same Ψ but they haven't proven that one V cannot yield more than one Ψ. Likewise they have proven that two Ψ's cannot give the same n but haven't proven that one Ψ cannot yield more than one n. Perhaps I'm missing something obvious but any insight would be appreciated.</p> https://mattermodeling.stackexchange.com/q/3557 8 Determine DFT+U values by linear response Tristan Maxson https://mattermodeling.stackexchange.com/users/697 2020-10-19T19:21:43Z 2020-10-19T21:22:19Z <p>I am currently trying to determine how DFT+U values can be determined self consistently. I see that the bare and converged linear response matrix contain the difference in occupancies from the ground state. I am wondering if my understanding of the approach is correct.</p> <ol> <li>Calculate ground state and record the final occupancies</li> <li>Calculate perturbed state (bare) by applying U to the ground state charge density then taking the first set of occupancies from the first SCF step as the bare response matrix (bare - ground)</li> <li>Converge the bare state and take the final set of occupancies as the converged response matrix (converged - ground)</li> </ol> <p>Then you end up with two matrices, the bare response matrix and the converged response matrix. The U value is then taken as follows.</p> <p>U = (Converged<sup>-1</sup>) - (Bare<sup>-1</sup>)</p> <p>My question is maybe naive, but how does this final step of subtracting two matrices give a U value.</p> <p>I am using <a href="http://hjkgrp.mit.edu/content/hubbard-u-multiple-sites" rel="noreferrer">this</a> as a resource to understand what is going on in this method.</p> <p>If it at all matters, I would like to use CASTEP or GPAW as my calculator.</p> https://mattermodeling.stackexchange.com/q/3554 4 Crystal structure prediction at finite temperatures for multiple atom alloys Hitanshu Sachania https://mattermodeling.stackexchange.com/users/116 2020-10-19T14:03:34Z 2020-10-19T14:03:34Z <p>Multiple atom alloys have become quite popular recently. The trend in modeling these materials is to represent them as random solid solutions. I use special quasirandom structures to model them. I wonder if this is appropriate. At each temperature, which configuration is the most stable is almost impossible to predict. Again, what trend would a material, that is disordered at <span class="math-container">$0$</span> K itself, follow as it heats?</p> https://mattermodeling.stackexchange.com/q/3550 9 Create doped structures to POSCAR files for vasp Binh Thien https://mattermodeling.stackexchange.com/users/1352 2020-10-19T10:43:33Z 2020-10-21T12:20:02Z <p>I have a perfect supercell structure Ni fcc. And I would like to dope another element O or N with a ratio of O:Ni=8:100 to the supercell of 3x3x3 and find the most stable ordering structure. The number of possible configurations is extremely large. I would like to ask if you know any code to generate all possible cases and remove of similar cases?</p> https://mattermodeling.stackexchange.com/q/3549 10 Comparing calculations in plane wave and atomic orbital bases for the same functional Rauz https://mattermodeling.stackexchange.com/users/1263 2020-10-19T10:40:48Z 2020-10-20T01:54:52Z <p>Let's say I have two codes. One is plane wave, and the other is using atomic orbitals as basis sets. How can I compare these two codes with the same functional? And let's say I want to optimise the same structure with these two codes (PBE). Principally they should give the same result, right?</p> https://mattermodeling.stackexchange.com/q/3545 7 What are some available software packages for automated finding of local and absolute minima on PES? Cavenfish https://mattermodeling.stackexchange.com/users/65 2020-10-19T03:25:48Z 2020-10-19T05:29:03Z <p>I have never used any AI driven calculation package before and to be honest don't fully understand the ins and outs of it. To be more specific I'm looking for something that can find local minima for molecules along with an absolute minima. I would like to get some quick exposure to some available packages (maybe some pros/cons) so that I can get a better idea of what exactly would be the ideal package for me.</p> <p>Please let me know what is available for this, and what exactly I should be looking out for (ie: ''Make sure you find AI driven not Monte Carlo based'').</p> <p>I would like to avoid calculating the whole PES, and would like to arrive at the minima more directly.</p> https://mattermodeling.stackexchange.com/q/3543 4 How to calculate the exchange matrix of a bilayer magentic system? Anoop A Nair https://mattermodeling.stackexchange.com/users/1355 2020-10-18T19:40:06Z 2020-10-18T21:53:42Z <p>I have been using the <a href="https://vampire.york.ac.uk/tutorials/bilayer-system-generation/" rel="nofollow noreferrer">Vampire atomistic simulation software</a> for a while and it has a useful simulation for predicting the curie temperature. But in order to do that I need the exchange matrix of Co-CoO system which should include the constant which represents the interaction between spins in the Co part, spins in the CoO part and a constant representing the interaction of spins in the Co-CoO interface. So I have been able to obtain the values for the Co and CoO part by couldn't find any resources to calculate the term representing the interaction at the interface. Should I use the average of the two values I've obtained. How should I approach this problem?</p> <p>An excerpt from the VAMPIRE manual regarding this is given below:</p> <blockquote> <p>material:exchange-matrix[index] = float [default 0.0 J/link] Defines the pair-wise exchange energy between atoms of type index and neighbour-index. The pairwise exchange energy is independent of the coordination number, and so the total exchange integral will depend on the number of nearest neighbours for the crystal lattice. The exchange energy must be defined between all material pairs in the simulation, with positive values representing ferromagnetic coupling, and negative values representing antiferromagnetic coupling. For a ferromagnet with a nearest neighbour exchange, the pairwise exchange energy can be found from the Curie temperature by the mean-field expression:</p> <p><span class="math-container">$$J_{ij} = \frac{(3k_{b}T_{c})}{\epsilon z}$$</span></p> <p>where <span class="math-container">$J_{ij}$</span> is the exchange energy, <span class="math-container">$k_{b}$</span> is the Boltzmann constant, <span class="math-container">$T_{c}$</span> is the Curie temperature, z is the coordination number (number of nearest neighbours) and <span class="math-container">$\epsilon$</span> is a correction factor to account for spin-wave fluctuations in different crystal lattices. If a custom unit cell file is used the exchange values defined here are ignored.</p> </blockquote> <p>I have included the manual here in this link. <a href="https://vampire.york.ac.uk/resources/vampire-manual.pdf" rel="nofollow noreferrer">VAMPIRE Manual</a></p> https://mattermodeling.stackexchange.com/q/3540 7 Apply DFT+U to Oxygen/Nitrogen p Orbitals Tristan Maxson https://mattermodeling.stackexchange.com/users/697 2020-10-18T15:27:49Z 2020-10-19T16:09:33Z <p>I often see DFT+U corrections for transition metals with delocalization problems, but a comment about applying a U correction to nitrogen or oxygen's p orbitals also shows up when digging around in the literature. It seems this is not done often, which makes understanding why and when it is done more difficult to search.</p> <p>When is this appropriate to consider?</p> https://mattermodeling.stackexchange.com/q/3539 7 DFT+U Values from Reference Formation Energies Tristan Maxson https://mattermodeling.stackexchange.com/users/697 2020-10-18T11:28:03Z 2020-10-19T21:47:20Z <p>I would like to calculate some DFT+U values for a few elements (Fe, Co, Ni, Cu, Zn?) and am attempting to take the approach from the <a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.73.195107" rel="nofollow noreferrer">Ceder 2006</a> paper. Where does the Ceder paper take its experimental values from? It seems to be from NIST and a &quot;Materials Thermochemistry&quot; book, but I am having trouble finding or accessing these.</p> <p>Is there a list compiled for the redox reactions in the Ceder paper? I could of course, just read the plot, but I would prefer a primary source for this data.</p> https://mattermodeling.stackexchange.com/q/3535 8 How can I do a charge transfer study in bulk crystalline materials? Hitanshu Sachania https://mattermodeling.stackexchange.com/users/116 2020-10-17T20:16:00Z 2020-10-18T18:01:03Z <p>Let's say a <em>supercell</em> has 100 atoms, and we want to see how charge is transferred from/to an atom to/from its nearest neighbours. I am more interested in knowing how to do this with density functional theory (DFT).</p> <p>Are charge-per-atom values, that any DFT code outputs, sufficient for the task?</p> https://mattermodeling.stackexchange.com/q/3523 7 Converge number of layers of atoms or number of unit cells for adsorption calculations? Charlie A https://mattermodeling.stackexchange.com/users/990 2020-10-16T15:25:43Z 2020-10-19T14:56:40Z <p>I'm trying to calculate the adsorption energies of various adsorbates on Fe2O3 surfaces (although, with there being many possible surfaces for Fe2O3 it is quite complex, but that's for another time...). I was wondering should I converge my adsorption energies versus the number of unit cells or add layers of atoms such that I am adding fractions of unit cells to the bottom of my slab? If its the case of unit cells, the Fe2O3 unit cell as a slab is already reasonably large, but doubling or even tripling it leads to it becoming unusably expensive for the resources I have available. Alternatively, adding atoms is less expensive but here we are breaking the stoichiometry of the unit cell.</p> <p>What would you advise?</p> <p>Here is my unit cell with</p> <ul> <li>A/B lengths = 5.035</li> <li>C length = 13.750.</li> </ul> <p><a href="https://i.stack.imgur.com/uk0MF.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/uk0MF.png" alt="Image of unit cell of Fe2O3" /></a></p> https://mattermodeling.stackexchange.com/q/3499 10 Bioisosteric replacement using SMARTS (KNIME and RDKit) Antoine Lacour https://mattermodeling.stackexchange.com/users/1420 2020-10-14T06:48:08Z 2020-10-18T13:22:14Z <p>I am trying to create a KNIME workflow that would accept a list of compounds and carry out <strong>bioisosteric replacements</strong> (we will use the following <em>example</em> here: <em>carboxylic acid to tetrazole</em>) automatically.</p> <blockquote> <p>NOTE: I am using the following workflow as inspiration : <a href="https://www.myexperiment.org/workflows/2683.html" rel="noreferrer">RDKit-bioisosteres (myexperiment.org)</a>. This uses a text file as SMARTS input. <strong>I cannot seem to replicate the SMARTS format used here.</strong></p> </blockquote> <p>For this, I plan to use the <a href="https://nodepit.com/node/org.rdkit.knime.nodes.onecomponentreaction2.RDKitOneComponentReactionNodeFactory" rel="noreferrer">Rdkit One Component Reaction</a> node which uses a set of compounds to carry out the reaction on as input and a SMARTS string that defines the reaction.</p> <p><strong>My issue is the generation of a working SMARTS string describing the reaction.</strong></p> <p>I would like to input two SDF files (or another format, not particularly attached to SDF): one with the group to replace (<em>carboxylic acid</em>) and one with the list of possible bioisosteric replacements (<em>tetrazole</em>). I would then combine these two in KNIME and generate a SMARTS string for the reaction to then be used in the <em>Rdkit One Component Reaction</em> node.</p> <blockquote> <p>NOTE: The input SDF files have the structures written with an attachment point (*COOH for the carboxylic acid for example) which defines where the group to replace is attached. I suspect this is the cause of many of the issues I am experiencing.</p> </blockquote> <p>So far, I can easily generate the reactions in RXN format using the <a href="https://nodepit.com/node/com.epam.indigo.knime.rbuilder.IndigoReactionBuilderNodeFactory" rel="noreferrer">Reaction Builder node</a> from the Indigo node package. However, converting this reaction into a SMARTS string that is accepted by the <a href="https://nodepit.com/node/org.rdkit.knime.nodes.onecomponentreaction2.RDKitOneComponentReactionNodeFactory" rel="noreferrer">Rdkit One Component Reaction</a> node has proven tricky.</p> <p>What I have tried so far:</p> <ol> <li><p>Converting RXN to SMARTS (<a href="https://nodepit.com/node/org.knime.chem.base.node.converter.parser.MolParserNodeFactory" rel="noreferrer">Molecule Type Cast</a> node) : gives the following error code : <code>scanner: BufferScanner::read() error</code></p> </li> <li><p>Converting the Source and Target molecules into SMARTS (<em>Molecule Type Cast</em> node) : gives the following error code : <code>SMILES loader: unrecognised lowercase symbol: y</code></p> <ul> <li>showing this as a string in KNIME shows that the conversion is not carried out and the string is of SDF format : <code>*filename*.sdf 0 0 0 0 0 0 0 V3000M V30 BEGIN</code> etc.</li> </ul> </li> <li><p>Converting the Source and Target molecules into RDkit first (<a href="https://nodepit.com/node/org.rdkit.knime.nodes.molecule2rdkit.Molecule2RDKitConverterNodeFactory" rel="noreferrer">RDkit from Molecule</a> node) then from RDkit into SMARTS (<a href="https://nodepit.com/node/org.rdkit.knime.nodes.rdkit2molecule.RDKit2MoleculeConverterNodeFactory" rel="noreferrer">RDkit to Molecule</a> node, SMARTS option). This outputs the following SMARTS strings:</p> <ul> <li>Carboxylic acid : <code>[#6](-[#8])=[#8]</code></li> <li>Tetrazole : <code>[#6]1:[#7H]:[#7]:[#7]:[#7]:1</code></li> </ul> </li> </ol> <p>This is as close as I've managed to get. I can then join these two smarts strings with <code>&gt;&gt;</code> in between (output: <code>[#6](-[#8])=[#8]&gt;&gt;[#6]1:[#7H]:[#7]:[#7]:[#7]:1</code>) to create a SMARTS reaction string but this is not accepted as an input for the Rdkit One Component Reaction node.</p> <blockquote> <p>Error message in KNIME console : ERROR RDKit One Component Reaction 0:40 Creation of Reaction from SMARTS value failed: null WARN RDKit One Component Reaction 0:40 Invalid Reaction SMARTS: missing</p> </blockquote> <p>Note that the SMARTS strings that this last option (3.) generates are very different than the ones used in the <a href="https://www.myexperiment.org/workflows/2683.html" rel="noreferrer">myexperiments.org example</a> (<code>[*:1][C:2]([OH])=O&gt;&gt;[*:1][C:2]1=NNN=N1</code>). I also seem to have lost the attachment point information through these conversions which are likely to cause issues in the rest of the workflow.</p> <p><strong>Therefore I am looking for a way to generate the SMARTS strings used in the myexperiments.org example on my own sets of substituents.</strong> Obviously doing this by hand is not an option. I would also like this workflow to use only the open-source nodes available in KNIME and not proprietary nodes (Schrodinger etc.).</p> <p>Hopefully, someone can help me out with this. If you need my current workflow I am happy to upload that with the source files if required.</p> <p>Thanks in advance for your help,</p> <p>Stay safe and healthy!</p> <p>-Antoine</p> https://mattermodeling.stackexchange.com/q/3492 13 Open Source PyMol Conda Package: UnsatisfiableError Tyberius https://mattermodeling.stackexchange.com/users/7 2020-10-13T17:36:36Z 2020-10-20T02:05:52Z <p>I'm having an issue installing the open-source version of Pymol using Anaconda on Windows. I have Python3.8.3 installed and when I try add the package with Conda using <code>conda install -c tpeulen pymol-open-source</code>, I get the error</p> <pre><code>UnsatisfiableError: The following specifications were found to be incompatible with the existing python installation in your environment: Specifications: - pymol-open-source -&gt; python[version='&gt;=3.7,&lt;3.8.0a0'] Your python: python=3.8 </code></pre> <p>Okay so it can't do python3.8? Not a problem, I'll just make a Python3.7 virtual environment <code>conda create -n &quot;py37&quot; python=3.7.9</code>. Except, for some reason this still returns an error.</p> <pre><code>UnsatisfiableError: The following specifications were found to be incompatible with the existing python installation in your environment: Specifications: - pymol-open-source -&gt; python[version='&gt;=2.7,&lt;2.8.0a0|&gt;=3.6,&lt;3.7.0a0|&gt;=3.8,&lt;3.9.0a0|&gt;=3.5,&lt;3.6.0a0'] Your python: python=3.7 </code></pre> <p>This claims that Python3.8 should work and the only version that doesn't work is 3.7! So, as a last attempt I tried switching to Python 3.6.12 and sure enough I got another error:</p> <pre><code>UnsatisfiableError: The following specifications were found to be incompatible with the existing python installation in your environment: Specifications: - pymol-open-source -&gt; python[version='&gt;=3.7,&lt;3.8.0a0'] Your python: python=3.6 </code></pre> <p>This says only 3.7 will work, but that is clearly not the case. Am I using conda wrong here or is there some issue with this package? How can I get the open-source PyMol installed properly?</p> <p>Note: There is Incentive PyMol released by Schrodinger, which I could successfully install through conda, but this is only a trial version. I would like to use the free version if possible.</p> https://mattermodeling.stackexchange.com/q/2469 11 What's the difference between spin-unpolarized, spin-polarized and non-colinear calculation? Jack https://mattermodeling.stackexchange.com/users/1218 2020-10-10T04:53:15Z 2020-10-19T20:01:15Z <p>The central goal of the first-principles simulation is to solve the Kohn-Sham equation:</p> <p><span class="math-container">$$[-\dfrac{1}{2}\nabla^2+v_{\textit{eff}}(\vec{r})]\phi_n(\vec{r})=E_n\psi_n(\vec{r}).$$</span></p> <p>Here the atomic unit has been adopted. But the previous equation can only be utilized to predict the properties of materials without magnetic properties and strong spin-orbit coupling (SOC). When the spin degree is considered, the simulation can be classified into three types accoding to the properties of materials:</p> <ul> <li>Spin-unpolarized [for non-magnetic with weak SOC materials];</li> <li>Spin-polarized [for magnetic materials];</li> <li>Noncolinear [for non-magnetic with strong SOC materials];</li> </ul> <p>But what's the essential difference between them? How can I understand them from the viewpoint of solving the Kohn-Sham equation?</p> https://mattermodeling.stackexchange.com/q/2453 9 Suggested cutoff for high level VASP calculations Alfred https://mattermodeling.stackexchange.com/users/984 2020-10-08T12:14:28Z 2020-10-20T16:35:31Z <p>I am new at VASP, and apparently, cutoff convergence is not something like kpoint convergence. Is using <code>ENCUT=1.5xENMAX</code> can be justified? For RPA, GW and MP2 calculations, I cannot use larger values.</p> https://mattermodeling.stackexchange.com/q/2433 6 Methods for visualization of crystal structures Hitanshu Sachania https://mattermodeling.stackexchange.com/users/116 2020-10-07T14:51:37Z 2020-10-18T22:23:55Z <p>Following up on <a href="https://mattermodeling.stackexchange.com/questions/467/which-are-the-freely-available-crystal-structure-visualization-softwares">this question about freely available crystal structure visualization codes</a>, I wish to know what <strong>procedures</strong> people follow from start to finish in preparing images of their materials for publication. A procedure can comprise of tools (codes and otherwise), colour schemes, angles, axes (or some other way to describe directions), lighting, and any further details, how much ever minute they may be.</p> <p>Let's keep answers restricted to one visualization software (e.g., <a href="https://jp-minerals.org/vesta/en/" rel="nofollow noreferrer">VESTA</a>) and accompanying details unless there's someone who uses two or more visualization software in tandem.</p> https://mattermodeling.stackexchange.com/q/2398 8 Why does my calculation using GROMACS get stuck at step 0? Kavya Mrudula https://mattermodeling.stackexchange.com/users/148 2020-10-01T10:42:50Z 2020-10-18T19:23:31Z <p>I have been using the HPCE on our campus for MD simulations using GROMACS. However, when I use the following script, after the job starts to run on the cluster, gets stuck at Step 0 in the log file.</p> <pre><code>#!/bin/bash #PBS -e errorfile.err #PBS -o logfile.log #PBS -l walltime=24:00:00 #PBS -l select=4:ncpus=6 cd $PBS_O_WORKDIR module load intel2020 gromacs2020.1 gmx mdrun -s npt.tpr -deffnm npt -v </code></pre> <p>The same would run smoothly if I submit the job with high number of processors like 40 or 80. However, there is generally a long queue for such high usage. What could be the reason for the job to not proceed beyond step 0 even after hours for lower number of processors?</p> <p>Example output files when I had terminated the system after 12min. The <code>.err</code> file:</p> <pre><code> GROMACS: gmx mdrun, version 2020.1 Executable: /lfs/sware/gromacs2020.1/bin/gmx Data prefix: /lfs/sware/gromacs2020.1 Working dir: /lfs/usrhome/phd/ch18d408/poly/long/350/1/try Command line: gmx mdrun -s npt.tpr -deffnm npt -v Reading file npt.tpr, VERSION 2020.1 (single precision) Changing nstlist from 10 to 100, rlist from 1 to 1 Using 6 MPI threads Non-default thread affinity set, disabling internal thread affinity Using 6 OpenMP threads per tMPI thread starting mdrun 'polymer' 500000 steps, 1000.0 ps. step 0 Received the TERM signal, stopping within 100 steps </code></pre> <p>And the <code>.log</code> file:</p> <pre><code> GROMACS: gmx mdrun, version 2020.1 Executable: /lfs/sware/gromacs2020.1/bin/gmx Data prefix: /lfs/sware/gromacs2020.1 Working dir: /lfs/usrhome/phd/ch18d408/poly/long/350/1/try Process ID: 15343 Command line: gmx mdrun -s npt.tpr -deffnm npt -v GROMACS version: 2020.1 Verified release checksum is 5cde61b9d46b24153ba84f499c996612640b965eff9a218f8f5e561f94ff4e43 Precision: single Memory model: 64 bit MPI library: thread_mpi OpenMP support: enabled (GMX_OPENMP_MAX_THREADS = 64) GPU support: CUDA SIMD instructions: AVX_512 FFT library: Intel MKL RDTSCP usage: enabled TNG support: enabled Hwloc support: disabled Tracing support: disabled C compiler: /lfs/sware/intel2019/compilers_and_libraries_2019.0.117/linux/mpi/intel64/bin/mpiicc Intel 19.0.0.20180804 C compiler flags: -xCORE-AVX512 -qopt-zmm-usage=high -mkl=sequential -std=gnu99 -ip -funroll-all-loops -alias-const -ansi-alias -no-prec-div -fimf-domain-exclusion=14 -qoverride-limits C++ compiler: /lfs/sware/intel2019/compilers_and_libraries_2019.0.117/linux/mpi/intel64/bin/mpiicpc Intel 19.0.0.20180804 C++ compiler flags: -xCORE-AVX512 -qopt-zmm-usage=high -mkl=sequential -ip -funroll-all-loops -alias-const -ansi-alias -no-prec-div -fimf-domain-exclusion=14 -qoverride-limits -qopenmp CUDA compiler: /lfs/sware/cuda-10.1/bin/nvcc nvcc: NVIDIA (R) Cuda compiler driver;Copyright (c) 2005-2019 NVIDIA Corporation;Built on Sun_Jul_28_19:07:16_PDT_2019;Cuda compilation tools, release 10.1, V10.1.243 CUDA compiler flags:-std=c++14;-gencode;arch=compute_30,code=sm_30;-gencode;arch=compute_35,code=sm_35;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_52,code=sm_52;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_35,code=compute_35;-gencode;arch=compute_50,code=compute_50;-gencode;arch=compute_52,code=compute_52;-gencode;arch=compute_60,code=compute_60;-gencode;arch=compute_61,code=compute_61;-gencode;arch=compute_70,code=compute_70;-gencode;arch=compute_75,code=compute_75;-use_fast_math;;-xCORE-AVX512 -qopt-zmm-usage=high -mkl=sequential -ip -funroll-all-loops -alias-const -ansi-alias -no-prec-div -fimf-domain-exclusion=14 -qoverride-limits -qopenmp CUDA driver: 0.0 CUDA runtime: N/A Running on 1 node with total 40 cores, 40 logical cores (GPU detection deactivated) Hardware detected: CPU info: Vendor: Intel Brand: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz Family: 6 Model: 85 Stepping: 7 Features: aes apic avx avx2 avx512f avx512cd avx512bw avx512vl clfsh cmov cx8 cx16 f16c fma hle htt intel lahf mmx msr nonstop_tsc pcid pclmuldq pdcm pdpe1gb popcnt pse rdrnd rdtscp rtm sse2 sse3 sse4.1 sse4.2 ssse3 tdt x2apic Number of AVX-512 FMA units: 2 Hardware topology: Only logical processor count ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hess, E. Lindahl GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers SoftwareX 1 (2015) pp. 19-25 -------- -------- --- Thank You --- -------- -------- ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ S. Páll, M. J. Abraham, C. Kutzner, B. Hess, E. Lindahl Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS In S. Markidis &amp; E. Laure (Eds.), Solving Software Challenges for Exascale 8759 (2015) pp. 3-27 -------- -------- --- Thank You --- -------- -------- ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ S. Pronk, S. Páll, R. Schulz, P. Larsson, P. Bjelkmar, R. Apostolov, M. R. Shirts, J. C. Smith, P. M. Kasson, D. van der Spoel, B. Hess, and E. Lindahl GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit Bioinformatics 29 (2013) pp. 845-54 -------- -------- --- Thank You --- -------- -------- ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ B. Hess and C. Kutzner and D. van der Spoel and E. Lindahl GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation J. Chem. Theory Comput. 4 (2008) pp. 435-447 -------- -------- --- Thank You --- -------- -------- ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ D. van der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark and H. J. C. Berendsen GROMACS: Fast, Flexible and Free J. Comp. Chem. 26 (2005) pp. 1701-1719 -------- -------- --- Thank You --- -------- -------- ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ E. Lindahl and B. Hess and D. van der Spoel GROMACS 3.0: A package for molecular simulation and trajectory analysis J. Mol. Mod. 7 (2001) pp. 306-317 -------- -------- --- Thank You --- -------- -------- ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ H. J. C. Berendsen, D. van der Spoel and R. van Drunen GROMACS: A message-passing parallel molecular dynamics implementation Comp. Phys. Comm. 91 (1995) pp. 43-56 -------- -------- --- Thank You --- -------- -------- ++++ PLEASE CITE THE DOI FOR THIS VERSION OF GROMACS ++++ https://doi.org/10.5281/zenodo.3685919 -------- -------- --- Thank You --- -------- -------- The number of OpenMP threads was set by environment variable OMP_NUM_THREADS to 6 Input Parameters: integrator = md tinit = 0 dt = 0.002 nsteps = 500000 init-step = 0 simulation-part = 1 comm-mode = Linear nstcomm = 100 bd-fric = 0 ld-seed = -464515634 emtol = 10 emstep = 0.01 niter = 20 fcstep = 0 nstcgsteep = 1000 nbfgscorr = 10 rtpi = 0.05 nstxout = 0 nstvout = 0 nstfout = 0 nstlog = 500 nstcalcenergy = 100 nstenergy = 500 nstxout-compressed = 500 compressed-x-precision = 1000 cutoff-scheme = Verlet nstlist = 10 pbc = xyz periodic-molecules = false verlet-buffer-tolerance = 0.005 rlist = 1 coulombtype = PME coulomb-modifier = Potential-shift rcoulomb-switch = 0 rcoulomb = 1 epsilon-r = 1 epsilon-rf = inf vdw-type = Cut-off vdw-modifier = Potential-shift rvdw-switch = 0 rvdw = 1 DispCorr = EnerPres table-extension = 1 fourierspacing = 0.16 fourier-nx = 128 fourier-ny = 128 fourier-nz = 128 pme-order = 4 ewald-rtol = 1e-05 ewald-rtol-lj = 0.001 lj-pme-comb-rule = Geometric ewald-geometry = 0 epsilon-surface = 0 tcoupl = V-rescale nsttcouple = 10 nh-chain-length = 0 print-nose-hoover-chain-variables = false pcoupl = Berendsen pcoupltype = Isotropic nstpcouple = 10 tau-p = 2 compressibility (3x3): compressibility[ 0]={ 4.50000e-05, 0.00000e+00, 0.00000e+00} compressibility[ 1]={ 0.00000e+00, 4.50000e-05, 0.00000e+00} compressibility[ 2]={ 0.00000e+00, 0.00000e+00, 4.50000e-05} ref-p (3x3): ref-p[ 0]={ 3.50000e+02, 0.00000e+00, 0.00000e+00} ref-p[ 1]={ 0.00000e+00, 3.50000e+02, 0.00000e+00} ref-p[ 2]={ 0.00000e+00, 0.00000e+00, 3.50000e+02} refcoord-scaling = COM posres-com (3): posres-com=-4.31721e-03 posres-com= 2.26425e-01 posres-com= 9.22131e-01 posres-comB (3): posres-comB=-4.31721e-03 posres-comB= 2.26425e-01 posres-comB= 9.22131e-01 QMMM = false QMconstraints = 0 QMMMscheme = 0 MMChargeScaleFactor = 1 qm-opts: ngQM = 0 constraint-algorithm = Lincs continuation = false Shake-SOR = false shake-tol = 0.0001 lincs-order = 4 lincs-iter = 1 lincs-warnangle = 30 nwall = 0 wall-type = 9-3 wall-r-linpot = -1 wall-atomtype = -1 wall-atomtype = -1 wall-density = 0 wall-density = 0 wall-ewald-zfac = 3 pull = false awh = false rotation = false interactiveMD = false disre = No disre-weighting = Conservative disre-mixed = false dr-fc = 1000 dr-tau = 0 nstdisreout = 100 orire-fc = 0 orire-tau = 0 nstorireout = 100 free-energy = no cos-acceleration = 0 deform (3x3): deform[ 0]={ 0.00000e+00, 0.00000e+00, 0.00000e+00} deform[ 1]={ 0.00000e+00, 0.00000e+00, 0.00000e+00} deform[ 2]={ 0.00000e+00, 0.00000e+00, 0.00000e+00} simulated-tempering = false swapcoords = no userint1 = 0 userint2 = 0 userint3 = 0 userint4 = 0 userreal1 = 0 userreal2 = 0 userreal3 = 0 userreal4 = 0 applied-forces: electric-field: x: E0 = 0 omega = 0 t0 = 0 sigma = 0 y: E0 = 0 omega = 0 t0 = 0 sigma = 0 z: E0 = 0 omega = 0 t0 = 0 sigma = 0 density-guided-simulation: active = false group = protein similarity-measure = inner-product atom-spreading-weight = unity force-constant = 1e+09 gaussian-transform-spreading-width = 0.2 gaussian-transform-spreading-range-in-multiples-of-width = 4 reference-density-filename = reference.mrc nst = 1 normalize-densities = true adaptive-force-scaling = false adaptive-force-scaling-time-constant = 4 grpopts: nrdf: 31229 ref-t: 298 tau-t: 0.5 annealing: No annealing-npoints: 0 acc: 0 0 0 nfreeze: N N N energygrp-flags[ 0]: 0 Changing nstlist from 10 to 100, rlist from 1 to 1 Initializing Domain Decomposition on 6 ranks Dynamic load balancing: locked Minimum cell size due to atom displacement: 0.703 nm Initial maximum distances in bonded interactions: two-body bonded interactions: 0.403 nm, LJ-14, atoms 30848 30853 multi-body bonded interactions: 0.403 nm, Ryckaert-Bell., atoms 30853 30848 Minimum cell size due to bonded interactions: 0.443 nm Maximum distance for 5 constraints, at 120 deg. angles, all-trans: 0.764 nm Estimated maximum distance required for P-LINCS: 0.764 nm This distance will limit the DD cell size, you can override this with -rcon Scaling the initial minimum size with 1/0.8 (option -dds) = 1.25 Using 0 separate PME ranks, as there are too few total ranks for efficient splitting Optimizing the DD grid for 6 cells with a minimum initial size of 0.956 nm The maximum allowed number of cells is: X 20 Y 20 Z 20 Domain decomposition grid 6 x 1 x 1, separate PME ranks 0 PME domain decomposition: 6 x 1 x 1 Domain decomposition rank 0, coordinates 0 0 0 The initial number of communication pulses is: X 1 The initial domain decomposition cell size is: X 3.33 nm The maximum allowed distance for atoms involved in interactions is: non-bonded interactions 1.000 nm (the following are initial values, they could change due to box deformation) two-body bonded interactions (-rdd) 1.000 nm multi-body bonded interactions (-rdd) 1.000 nm virtual site constructions (-rcon) 3.333 nm atoms separated by up to 5 constraints (-rcon) 3.333 nm When dynamic load balancing gets turned on, these settings will change to: The maximum number of communication pulses is: X 1 The minimum size for domain decomposition cells is 1.000 nm The requested allowed shrink of DD cells (option -dds) is: 0.80 The allowed shrink of domain decomposition cells is: X 0.30 The maximum allowed distance for atoms involved in interactions is: non-bonded interactions 1.000 nm two-body bonded interactions (-rdd) 1.000 nm multi-body bonded interactions (-rdd) 1.000 nm virtual site constructions (-rcon) 1.000 nm atoms separated by up to 5 constraints (-rcon) 1.000 nm Using 6 MPI threads Non-default thread affinity set, disabling internal thread affinity Using 6 OpenMP threads per tMPI thread System total charge: -0.000 Will do PME sum in reciprocal space for electrostatic interactions. ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ U. Essmann, L. Perera, M. L. Berkowitz, T. Darden, H. Lee and L. G. Pedersen A smooth particle mesh Ewald method J. Chem. Phys. 103 (1995) pp. 8577-8592 -------- -------- --- Thank You --- -------- -------- Using a Gaussian width (1/beta) of 0.320163 nm for Ewald Potential shift: LJ r^-12: -1.000e+00 r^-6: -1.000e+00, Ewald -1.000e-05 Initialized non-bonded Coulomb Ewald tables, spacing: 9.33e-04 size: 1073 Generated table with 1000 data points for 1-4 COUL. Tabscale = 500 points/nm Generated table with 1000 data points for 1-4 LJ6. Tabscale = 500 points/nm Generated table with 1000 data points for 1-4 LJ12. Tabscale = 500 points/nm Using SIMD 4x8 nonbonded short-range kernels Using a 4x8 pair-list setup: updated every 100 steps, buffer 0.000 nm, rlist 1.000 nm At tolerance 0.005 kJ/mol/ps per atom, equivalent classical 1x1 list would be: updated every 100 steps, buffer 0.000 nm, rlist 1.000 nm Using Lorentz-Berthelot Lennard-Jones combination rule Long Range LJ corr.: &lt;C6&gt; 5.0152e-04 Removing pbc first time Initializing Parallel LINear Constraint Solver ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ B. Hess P-LINCS: A Parallel Linear Constraint Solver for molecular simulation J. Chem. Theory Comput. 4 (2008) pp. 116-122 -------- -------- --- Thank You --- -------- -------- The number of constraints is 6385 There are constraints between atoms in different decomposition domains, will communicate selected coordinates each lincs iteration Linking all bonded interactions to atoms There are 18465 inter update-group virtual sites, will an extra communication step for selected coordinates and forces Intra-simulation communication will occur every 10 steps. ++++ PLEASE READ AND CITE THE FOLLOWING REFERENCE ++++ G. Bussi, D. Donadio and M. Parrinello Canonical sampling through velocity rescaling J. Chem. Phys. 126 (2007) pp. 014101 -------- -------- --- Thank You --- -------- -------- There are: 12539 Atoms There are: 18465 VSites Atom distribution over 6 domains: av 5167 stddev 199 min 4989 max 5507 Constraining the starting coordinates (step 0) Constraining the coordinates at t0-dt (step 0) Center of mass motion removal mode is Linear We have the following groups for center of mass motion removal: 0: rest RMS relative constraint deviation after constraining: 2.31e-05 Initial temperature: 300.037 K Started mdrun on rank 0 Fri Oct 2 15:05:27 2020 Step Time 0 0.00000 Energies (kJ/mol) Angle Ryckaert-Bell. Improper Dih. LJ-14 Coulomb-14 4.45652e+02 -4.13382e+02 2.39002e+00 5.76587e+02 3.22453e+02 LJ (SR) Disper. corr. Coulomb (SR) Coul. recip. Position Rest. </code></pre> <p>You can also download the <code>.err</code> and <code>.log</code> files <a href="https://www.dropbox.com/sh/q8qhimo0adag0ry/AABGezTsqwCnNkEcbm6L3W18a?dl=0" rel="nofollow noreferrer">here</a>. The job would just get killed after the time allocated if I do not terminate.</p> https://mattermodeling.stackexchange.com/q/2389 8 How to interpret second order perturbation theory analysis from NBO calculations? farmaceut https://mattermodeling.stackexchange.com/users/1247 2020-09-29T20:14:03Z 2020-10-18T19:15:59Z <p>The question is quite simple — how to interpret NBO results of second order perturbation theory analysis? As I have read several papers the most important interactions are <span class="math-container">$\pi\rightarrow\pi^{*}$</span>, <span class="math-container">$LP\rightarrow\sigma^{*}$</span> and <span class="math-container">$LP\rightarrow\pi^{*}\$</span>. However, how to check whether they corresponds to resonance or hyperconjugation effect?</p> <p>In the paper of Milenković et al. (10.20450/mjcce.2019.1333), for structure below: <a href="https://i.stack.imgur.com/vhx6G.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/vhx6G.png" alt="enter image description here" /></a></p> <p>A following data of NBO-analysis was gathered: <a href="https://i.stack.imgur.com/ieoCv.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ieoCv.png" alt="enter image description here" /></a></p> <p>The authors claim that:</p> <blockquote> <p>The most important interaction (n-π*) energy, related to resonance in the molecule, is electron donation from the LP2(O) atom orbitals to the antibonding acceptor π*(C–C) of the phenyl ring (LP2(O5) → π*(C5–C6) (159.2 kJ mol–1)). This large interaction energy indicates hyperconjugation between the electron-donating oxygen atom and the phenyl ring.</p> </blockquote> <p>Later similarly:</p> <blockquote> <p>Besides LP2(O) → π*(C–C) and LP2(O) → σ*(C–C) interactions, strong intra- molecular hyperconjugative interactions are formed by orbital overlap between π(C–C) → π*(C–C) bond orbitals, resulting in intramolecular charge transfer (ICT), which causes stabilization of the system.</p> </blockquote> <p>The problem for me is how to distinguish hyperconjugation from resonance? I was sure that, quoting wiki –</p> <blockquote> <p>... hyperconjugation is the interaction of the electrons in a sigma (σ) orbital (e.g. C–H or C–C) with an adjacent unpopulated non-bonding p or antibonding σ* or π* orbitals to give a pair of extended molecular orbitals. Thus, why would LP2(O) → π*(C–C) be hyperconjugation if it is rather resonance?</p> </blockquote> <hr> <p>I have also found somewhere about <code>Intramolecular Charge Transfer</code>. Could someone briefly explain how it can be understood and found in this analysis?</p> <p>Kind regards</p> https://mattermodeling.stackexchange.com/q/2387 9 A viscoelastic material with nonconvex memory kernel? Lorenzo Liverani https://mattermodeling.stackexchange.com/users/1386 2020-09-29T15:46:10Z 2020-10-18T18:52:17Z <p>The title is basically my question. Viscoelastic materials are characterized by a constitutive equation between stress and strain involving a convolution integral. This integral is weighted with a relaxation kernel (or memory kernel).</p> <p>Usually, the memory kernel is convex and decreasing (actually, it is often an exponential), but I was wondering if there exists materials for which the memory kernel is nonconvex.</p> <p>Thank you very much to anyone willing to respond!</p> https://mattermodeling.stackexchange.com/q/2386 8 How to predict new Half Metallic materials with higher degree of spin polarization? Ujjawal M. https://mattermodeling.stackexchange.com/users/1361 2020-09-29T09:40:19Z 2020-10-18T18:41:14Z <p>Using DFT calculations, how one can predict the new half metallic materials with higher degree of spin polarization. I want to know the steps which have to be followed in prediction of new materials. I have read some research papers on half metallics, but I don't know the exact procedure to start with.I have done some basic DFT calculations on Si, diamond, Fe and Al.<br /> Please, someone suggest me where and how to start predicting HMF materials.<br /> Thanking you!</p> https://mattermodeling.stackexchange.com/q/2371 6 Is it possible to simulate the Raman spectra via Molecular Dynamics (MD) using the GROMACS package? Anoop A Nair https://mattermodeling.stackexchange.com/users/1355 2020-09-26T15:04:52Z 2020-10-19T14:44:22Z <p>I'm new to molecular dynamics and DFT. The aim was to study the Raman spectra of a molecule in a medium. As we know the Raman spectra of a molecule change due to the induced polarization in the presence of different solvent molecules. Since the GROMACS package can analyse molecules present in different solvents we can create boxes containing the molecule and different solvents. But since I'm new to the software I'm not sure if Raman Spectra generation is possible. Or is there some other software package I could use to fulfil the needs mentioned above.</p> <p>Thanks in advance.</p> https://mattermodeling.stackexchange.com/q/2320 8 Inconsistent data of chemical potential from different papers Binh Thien https://mattermodeling.stackexchange.com/users/1352 2020-09-20T03:35:23Z 2020-10-18T18:33:41Z <p>I am looking for chemical potential from literature:</p> <pre><code>(1) https://journals.aps.org/prb/abstract/10.1103/PhysRevB.85.115104 (2) https://www.nature.com/articles/npjcompumats201510 </code></pre> <p>I compared the method and data they provided. They use the Fitted Elemental Reference Energy method via GGA+U on VASP employing PAW. U values in these paper are a little different. As far as I understand from the papers, the chemical potential is equal to total energy of an isolated atom in a box (for example, 10x10x10). Am I right? Then I think it should be similar. However, found that the data are somehow inconsistent.For example:</p> <pre><code> Paper (2) (Table 1) Paper (1) (Table V: Appendix) Fe 2.200 -6.15 Mn 1.987 -7.00 Co 1.987 -4.75 </code></pre> <p>Can anyone help me to explain?</p> https://mattermodeling.stackexchange.com/q/1630 7 What are the types of Quantum Molecular Dynamics (QMD)? Etienne Palos https://mattermodeling.stackexchange.com/users/175 2020-07-23T00:32:17Z 2020-10-20T04:04:24Z <p>In similar spirit to recent questions on <a href="https://mattermodeling.stackexchange.com/questions/1594/what-are-the-types-of-quantum-monte-carlo">Quantum Monte Carlo</a>, <a href="https://mattermodeling.stackexchange.com/questions/1523/what-are-the-types-of-ab-initio-molecular-dynamics"><em>ab-initio</em> Molecular Dynamics</a>, types of <a href="https://mattermodeling.stackexchange.com/questions/1568/what-are-the-types-of-scf">SCF</a>, and others, I would like to ask:</p> <p><strong>What are the types of Quantum Molecular Dynamics (QMD)?</strong></p> <p>As I have recently learned, Quantum Molecular Dynamics is different to AIMD and MD through the fact that in QMD, the <em>nuclei</em> are treated quantum mechanically.</p> <p>I would like to ask here, if we can summarize the types of QMD in a few paragraphs:</p> <ul> <li>PIMD (Feynman Path Integral Molecular Dynamics)</li> <li>CMD (Centroid Molecular Dynamics)</li> <li>RPMD (Ring Polymer Molecular Dynamics) [<a href="https://mattermodeling.stackexchange.com/a/3546/5">link to answer</a>]</li> <li>*Feel free to let me know of other methods or include them as an answer!</li> </ul> <p>Also, it would be great if we limit one QMD method per answer (and per person).</p> https://mattermodeling.stackexchange.com/q/1354 15 Is there a set of updated, comprehensive benchmarks for speed comparison between different quantum chemistry packages? ksousa https://mattermodeling.stackexchange.com/users/243 2020-06-19T23:06:36Z 2020-10-21T17:58:30Z <p>I've found a page dedicated to quantum chemistry packages benchmarks, on GitHub, <a href="https://github.com/PedroJSilva/qmspeedtest" rel="noreferrer">qmspeedtest</a>. But most results there are several years old, and so probably outdated. Is there some place where we can find comparisons like these, but updated often, or at least more recently?</p> <p>I specified quantum chemistry in the question because I'm more interested in molecular systems, modeled with atom-centered gaussian function basis sets, for example. I have almost no familiarity with software that deals with periodic systems, plane-wave based. But I think it could be a good idea if someone with more familiarity with periodic systems opened a similar question for the respective packages.</p> https://mattermodeling.stackexchange.com/q/1122 14 Running Quantum ESPRESSO calculations in Google Colab jboy https://mattermodeling.stackexchange.com/users/347 2020-05-29T07:42:00Z 2020-10-19T22:40:41Z <p>So one big problem I experience in doing calculations with Quantum ESPRESSO is that many calculations require a lot of computing power: you need large RAM capacities and powerful processors especially when there are many atoms in a solid state model and the computation requires a dense k-point grid. It becomes really impractical for a desktop setup at some point.</p> <p>Some peers of mine from the computer science department of our university said that since we don't have an HPC here (I'm from a 3rd world country), what they do is they run their programs (which they wrote themselves) in free cloud computing services such as the Google Colab. </p> <p>My question is this: Is there a way to run Quantum Espresso on Google Colab?</p> https://mattermodeling.stackexchange.com/q/778 13 Derivatives with respect to user defined vibrational modes Tyberius https://mattermodeling.stackexchange.com/users/7 2020-05-17T21:20:27Z 2020-10-18T23:14:23Z <p>I'm doing some property calculations that depend on a sum of derivatives of some quantity with respect to normal vibrational modes. I was hoping to find some physical intuition relating the type of mode to its property contribution, but there isn't an obvious connection in the normal mode basis.</p> <p>I decided to try converting to a different mode basis to see if there is a more obvious connection. I'm able to convert the modes using a unitary transformation, but I can't seem to convert derivatives to the new basis properly (the unitary transformed derivatives don't match numerical derivatives along the transformed modes).</p> <p>I asked separately <a href="https://mattermodeling.stackexchange.com/questions/928/discrepancy-between-numerical-and-transformed-derivatives">here</a> about any errors with my transformation method? For this question, I'm more curious if this already solved in an existing program? Is there an electronic structure program that can perform derivatives with respect to vibrational modes and allows the user to define the modes?</p> https://mattermodeling.stackexchange.com/q/483 16 How to calculate t-zero temperature using Thermo-Calc- Python (TCPython) for multiple alloys? Hariharan https://mattermodeling.stackexchange.com/users/354 2020-05-09T02:50:28Z 2020-10-20T16:06:27Z <p>T-zero temperature is the temperature at which the Gibbs energies of two phases are equal. Here, I wish to find the temperature at which FCC and BCC have same Gibbs energy. Thermo-Calc console mode has an in-built function to calculate t-zero. However, if I manually do the calculation in TC-Python the results are different.</p> <pre class="lang-py prettyprint-override"><code>import numpy as np from tc_python import * w_c=0.008 with TCPython() as start: # create and configure a single equilibrium calculation calc_result = ( start .set_cache_folder(os.path.basename(__file__) + &quot;_cache&quot;) .select_database_and_elements(&quot;TCFE6&quot;, [&quot;Fe&quot;,&quot;C&quot;]) .get_system() .with_single_equilibrium_calculation() .disable_global_minimization() .set_condition(ThermodynamicQuantity.mass_fraction_of_a_component(&quot;C&quot;), w_c) ) step=2000 for temp in np.linspace(500,1200,step): calc = ( calc_result .set_condition(ThermodynamicQuantity.temperature(),temp) .calculate() ) gf = abs(calc.get_value_of('GM(FCC_A1)')) gb = abs(calc.get_value_of('GM(BCC_A2)')) if(abs(gf-gb)&lt;=1): print(temp,gf,gb) break </code></pre> <p>There was no temperature that satisfied this criteria. However, in console mode (adv-options t-zero bcc_A2 fcc_A1 )I got 784.79 K. If I use run_poly_command also, I get the set-temperature (473 K) and not t-zero temperature.</p> <pre class="lang-py prettyprint-override"><code>from tc_python import * w_c=0.008 with TCPython() as start: # create and configure a single equilibrium calculation calc_result = ( start .set_cache_folder(os.path.basename(__file__) + &quot;_cache&quot;) .select_database_and_elements(&quot;TCFE6&quot;, [&quot;Fe&quot;,&quot;C&quot;]) .get_system() .with_single_equilibrium_calculation() .disable_global_minimization() .set_condition(ThermodynamicQuantity.mass_fraction_of_a_component(&quot;C&quot;), w_c) .set_condition(ThermodynamicQuantity.temperature(),473) ) calc=calc_result.run_poly_command('adv t-zero bcc_a2 fcc_a1').calculate() print(calc.get_value_of('T')) </code></pre> <p>What command should be used to get t-zero temperature in TC-Python?</p>