r/comp_chem

Hi all,

ORCA newbie here who has some experience using Gaussian (I am primarily an organic chemist so be kind lol)
I'm looking to model some larger molecules (50 atoms +), and struggling with long computational run times with this level of theory:
WB97X-D3 OPT def2-TZVP
Is there a way for me to significantly speed up these calculations by allocating jobs over multiple nodes or anything?
If anyone does similar jobs, how many nodes/ CPUs do ppl usually allocate in their slurm scripts. And if anyone can send me an example of a slurm script used to submit these jobs that would be very appreciated. I'm running this through my institutes supercomputer so no real issue with using lots of processing power
Thanks all

This is just a vent no questions or anything, just telling you what I did lastly, when I accepted the offer to do master studies in material science experiments lab at theat I just knew the basics things of computational chemistry what's it and the science behind and to be honest I was in love in this field but I studied as self learning, anyway when I accepted the master offer and came to the uni, the PI told me you'll do DFT studies with experiments (he didn't tell me that in the interview), however I was happy but I shocked the PI and the university I'm in and the co-supervisor no one expert in DFT and they don't have the resources to do it such clusters or even manageable computer, I didn't stop at this point to blame or giving up, literally I spent hours and hours studying DFT even in weekends I don't go out I spent most of the hours studying running DFT functionals slabs and I went through a lot of simulations using my own computer, then I noticed my computer won't be enough to run really good results I searched for possible computers or collaborators I found one I gave it to my PI and we collobrated with him but his local computer was really slow and sometimes for small results takes month and half, but then I found a grant to use a cluster I applied to it and now I got approval for this grant.

I feel like I have established the ground basis for the lab and gave them the opportunity to explore the computational chemistry world, and I fell I'm proud of my self I didn't give up.

Hello, I need to do molecular dynamics simulation for several proteins with non-canonical, modified amino acid residues. For example: PDB IDs 1ATN and 1VIB in RCSB database. The modifications for protein residues can come from biologically post-translational modification (PTM) (phosphorylation, glycosylation, etc.) or artificially attaching small molecules via covalent bonds (such as fluorescent proteins). My questions are:

1. In principle, what are the steps to simulate modified residues? How to do force field parameterization for modified amino acids and integrate the force field for the modified residues with the force field of the canonical residues for the rest of the protein?

2. Does the method for force field parameterization differ between PTM or artificial attachment of small molecules?

3. I'm using OpenMM to simulate. Is there a well-established protocol to simulate modified residues within the OpenMM software ecosystem?

Thank you for reading my questions.

Hi everyone,

Do people actually make money by doing DFT reaction profiles/mechanistic calculations for other researchers?

I mean things like TS search, IRC validation, energy profiles, and pathway comparison for organic reactions.

Is there real freelance/consulting demand for this? And where do people usually find such work — Reddit, Upwork, LinkedIn, or direct academic collaborations?

Any advice from people who have done paid computational chemistry work would be helpful.

I am trying to open a sio2 alpha quartz structure cif file in avogadro, i converted the file from cif to xyz format and tried opening it in avogadro but it is running into error saying error parsing number of atoms, can anyone guide me how to solve it

I have recently completed a PhD in cheminformatics in the UK, and I am trying to get out of academia. Most of the job openings require at least 3 years of post-PhD industrial experience, so there's a bit of a vicious circle.

The industrial collaboration with the funders did not pan out, and the PI insisted that any external communications go through them, so I mostly did my own research without really caring about how 'fashionable' it is and missed out on MD/FEP/co-folding/diffusion methods, which appear to be all the rage these days. I had some experience with distributing my software as a Python package but not as a container

What would be the highest-impact thing I could do to get my foot in the door in the industry? I was thinking of distilling an MLIP into a 2D GNN and deploying it online, but there might be more 'interesting' things for the prospective employers or better ways to distribute it

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Hi comp_chemists!

We (Paton Lab at Colorado State) developed goodvibes several years ago for applying Grimme & Truhlar style entropy corrections to compchem outputs.

Additional features were gradually added over time (plotting energy profiles, selectivity predictions, deduplicating and conformers), although the code became slower and bit clunky.

We went back to the lab and revamped the code, and recently released goodvibes version 4. Hopefully users will find parsing much faster and greater stability over different outputs (orca5, orca6, g16, qchem6, ase, xTB). The commandline version should look familiar: however, we substantially updated the python API making it much easier to import and call goodvibes from within other python projects or notebooks. Some examples are in the updated read-the-docs.

For the up-to-date version, pip install git+https://github.com/patonlab/GoodVibes.git

Anyway, please let us know bugs, feature requests etc!

Title says/asks it all, but I'll expound.

I'm trying to decide on a concentration for grad school and comp chem is one option I'm considering (along with organic chem and analytical chem).

I can't say I find comp chem extremely interesting, but part of that may just be me feeling overwhelmed because I don't have a strong physics or math (or programming) background.

I try to work on learning more math and programming in my free time, but physics is honestly not my cup of tea. Not even in the slightest.

I'm reading papers from various labs that I feel I may/would want to join and the papers from labs that are mainly focused on using comp chem are the hardest papers to stay engaged with.

In theory, I love the idea of the power comp chem holds, to perform/run many reactions in a short amount of time, and make predictions it would take a long time for humans to decide on.

However, I'm not sure if finding comp chem "cool" and powerful is going to be enough of a motivator to actually gain any competence in the topic.

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I've been running SPE for the same geometry on ORCA with different methods and levels of theory for some benchmarking/getting used to an HPC as I am new to all that, and for whatever reason my files that work fine for HF/DFT run up an error on semiempirical methods, namely AM1 and PM3. This calculation is of a ghost monomer and another real atom monomer (for BSSE purposes), so it is of the format where ghost atoms are denoted with a ":" following the element symbol in xyz notation. However, when I attempt to run AM1 or PM3 with the *exact* same input except the change in method I receive the following error

[file orca_main/main_util_tools.cpp, line 710]: Error : multiplicity (1) is odd and number of electrons (187) is odd -> impossible

Any ideas what could be going wrong here? The coordinate files are 100% identical. I've pasted a working and non-working ! block example below

This one worked.

$new_job
! RHF 6-311++G** pmodel PAL8
%maxcore 15000
%base "monomer1_ghost"
%id "monomer1_ghost"

This one did not.

$new_job
! PM3 pmodel NoAutoStart 
%maxcore 15000
%base "monomer1_ghost"
%id "monomer1_ghost"

I hear a lot in the Dft community about research trying to achieve chemical accuracy for different molecules, usually diatomic molecules. How are they trying to achieve this in practice? You can use an usual software like gaussian or orca etc, trying different functionals, cumbining them and so on, or they are making first principle calculations on their own?

Plzzz share your experience

I made a small VS Code extension for visualizing VASP structure files (POSCAR / CONTCAR / .vasp) directly inside the editor.

This mostly came from my own frustration of repeatedly exporting structures to VESTA just to quickly check edits while working in VS Code, so I thought I’d share it in case it’s useful to anyone else with a similar workflow :)

The preview updates automatically as the file changes and currently supports basic bond visualization, atom hover coordinates inspection, and selective dynamics highlighting.

VS Marketplace:
https://marketplace.visualstudio.com/items?itemName=PurunSimonCao.atomview

GitHub:
https://github.com/Simon-Cao-Git/AtomView

I'm a bsc chemistry graduate. Is there any course to make 50-60k rupees per month as a starting salary after Msc chemistry. Is pursuing msc chemistry could give me such job?

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Hi I did a molecular docking run using a tetramer and trimer of a polymer. results show that both trimer and tetramer bonded onto the same binding pocket, what could be the implicaction to this? is this an implication that the protiens active site prefers bigger molecules

Hi all,

I am currently designing de novo proteins based on a wild type. I want to use MD to check if the mutations I am adding are likely to have destabilised the protein. This is so I can have a rationale for reducing my design space, and prioritise variants to express at scale.

My proteins are approx 97 amino acids long.

I am currently in a production run cycle - my plan is to run 5 x 200ns simulations for each protein variant and compare RMSD, Rg, RMSF, and hydrogen bond number throughout the run to infer improved or reduced stability. RMSF for functional regions specifically I.e. what do the variations do to rigidity of the scaffold and the functional region.

I have expressed the proteins and will be experimentally validating the runs by testing thermostability and activity of the proteins in vitro.

For people who are fluent in MD - does this sound like something that would hold up and be defensible?

Thanks for your help!

Hi,

I would like to perform metadynamics to a gpcr bound in a lipid bilayer to a protein ligand which I docked to the receptor. From a paper I know the structural differences between the active and inactive receptor.

From what I understand would be good practice to:

- Show that running unbiased MD does not show the activation of the GPCR.

- Run also the receptor without any ligand to show the energy difference with and without the ligand

- Run a negative control with a protein who supposedly does not activate the receptor

- Run the MD in triplicates.

Since keeping up with all these practices would mean a lot of computational power that since I am using my university HPC that implies a lot of queuing and stuff. How long should i run unbiased and meta md? Should i do triplicates? Is it really important to run a negative control?

And for the one experienced in metaMD, how do i pick a CV that makes sense? And other tips?

Hello, I have a very limited exposure to growmacs and wanted to see if I can simulate a lipid nanoparticle for fun. I do not have a hard cord chemistry background but have a little bit of bio and biochem courses under my belt. I saw a few papers on this but it seemed to be pretty in depth which I do not have the background for. Is there a tutorial on somewhere if I have not seen yet that you would recommend?

I took a look at the growmacs tutorial http://www.mdtutorials.com/gmx/ but there isn't really anything on spherical nanoparticles with lipids with mRNA encapsulated in it. If you happen to any resources please let me know!

I will keep looking in the meantime.

Thank you in advance!