r/comp_chem

CS 50 before comp chem

Would it benefit me to first take CS50x and CS50 Introduction to Python courses, and learn coding generically first, as someone who's interested in comp chem and bioinformatics, but has absolutely zero experience in coding? Or would you suggest jumping into the core material directly and learning python alongside?

I do have strong theoretical foundations in linear algebra, differential equations and stat thermo, or so I believe.

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u/PensionMany3658 — 11 hours ago
▲ 6 r/comp_chem+2 crossposts

I trained a local AI model that generated 22,000+ novel drug-like molecules — verified against 4.6M known compounds. Dataset available.

Built an 80M parameter causal transformer on consumer hardware (RTX 5070), trained on MOSES + ZINC-250k. Generated and filtered for QED ≥ 0.5, SA ≤ 4.0, MW ≤ 500. Top compound hits QED 0.947. 100% novel against MOSES, ZINC, and ChEMBL.

HuggingFace: https://huggingface.co/datasets/MKEChem/mke-novel-druglike-smiles

Happy to answer questions about the generation method.

u/ChemMKE — 11 hours ago

Claude Science is cool, but I’m wondering what this looks like outside bio

The Claude Science launch is exciting, but almost all the use cases seem heavily focused on bio and genomics. For those of us in chemistry, materials, or battery R&D, the real pain point isn’t just summarizing papers—it’s the messy middle of connecting disparate literature, simulation data, and long-running calculations. I recently saw a tool called sciclaw·mira (maybe by deep principle) that focuses more on this project-level workflow and memory specifically for materials/chem.

For people in these fields, what would actually move the needle for you: a better workbench for answering questions, or an AI that manages the whole research loop without losing context?

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u/Scared_Skirt9455 — 2 days ago
▲ 22 r/comp_chem+4 crossposts

BatteryMHM: a 557-feature "harmonic" descriptor that beats a deep NeuralODE on battery state-of-health — CPU-only, no weights

I’ve open-sourced the method behind a battery state-of-health model that, somewhat annoyingly for my own priors, beats a published deep net on a standard benchmark using only tree ensembles on CPU.

The idea. Instead of feeding raw cycling curves to an RNN/transformer, I fold every measurement into a 9-class “harmonic” space (HIN(k) = 1 + ((k−1) mod 9)), score pairwise interactions through a fixed 9×9 compatibility matrix, and aggregate into a 557-dim descriptor (Chi histograms, Markov transitions, a Miller-sequence multi-scale calculus, entropy). Then ExtraTrees + XGBoost.

Result (MIT–Stanford–TRI / Severson et al., Nature Energy 2019, 144 cells, 5-fold CV, 30% observation window ≈ 45 cycles):

|Model |MAE |RMSE |PCC |R² |

|This method |**0.0114**|**0.0200**|0.884|0.747|

|Attentive NeuralODE (Li 2021) |0.012 |0.020 |0.900|0.810|

|RF (Microsoft BatteryML, ICLR’24)|0.2459 |0.3140 |0.610|0.269|

Wins MAE/RMSE; still behind the NeuralODE on PCC/Spearman/R² (it’s not a clean sweep). 21.6× lower MAE than BatteryML’s strongest sklearn baseline, with a shorter window.

Honest limitations. On the materials track (Matbench mp_e_form) the same descriptor gets 0.1513 eV/atom — beats the classic RF+Magpie baseline but is well behind modern GNNs (CGCNN/CHGNet). The bundled demo is synthetic (a signal check, not the benchmark). No trained weights are shipped — you train your own (seconds, CPU). License is CC-BY-NC-4.0 and the method is patent-pending, so it’s “open to read/run/research,” not OSI-open — flagging that up front.

Repo (method, demo, tests, docs): https://huggingface.co/williamTLmiller/batterymhm

pip install "git+https://huggingface.co/williamTLmiller/batterymhm"

python demo.py

I’m genuinely curious about: is the win mostly the modular fold-map representation, or just that trees beat small-data deep nets on ~144 cells? I’d love for people to (a) try the descriptor on other sequence/tabular tasks, or (b) find their own way past 0.0114. Challenge thread is in the repo’s Community tab.

u/Ornery-Control2855 — 5 days ago

Anthropic just released Claude Science

Today, Anthropic announced the beta release of Claude Science, which they describe as "an AI workbench for scientists."

I haven't used it yet, but from what I've seen, it looks very much like a Jupyter notebook with Claude built-in. Thoughts?

u/micmalti — 5 days ago

MD simulations with nucleic acids

Hi! I normally work with systems limited to proteins and small molecules, but recently I was reached out for collaboration that focuses on studying interactions between proteins and nucleic acids. I already got my simulation plan ready, but I was wondering if there are any special caveats I should keep in mind when working with such systems. It sounds pretty straightforward to me since the majority of the FFs already include parameters for nucleic acids, but recently I discovered, for instance, that zinc-oriented proteins are not represented correctly by CHARMM36 due to zinc ion recruiting two water molecules leading to disruption of zinc-oriented shell and if I wanted to simulate such system I would have to use special force fields like (E)ZAFF or use distancs restraints/dummy molecules. I was wondering if there are any special considerations of this kind when it comes to MD simulations involving nucleic acids as well. For context, I plan to use CHARMM36 ported for GROMACS, but I am ready to adjust (not sure about learning new software, though). Thank you in advance!

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u/hexagon12_1 — 6 days ago

Getting Start with Computational Methods for solid-state-batteries

Hi all,

Recently I have started a PhD project focusing on high performance solid state batteries. I have a research target given to me as provided by my supervising professor but I have been given quite a lot of freedom in what direction I take my research and what methodologies I could use.

The long and short of it is I would like to implement computational methods into my research in order to do bulk analysis of lithium migration through electrode and electrolytes. As well as performing solid-solid surface interaction simulations. I see that a significant amount of publications will usually include DFT calculations or MD simulations to assist in explaining the results and back-up claims.

Unfortunately, there is not anyone within my faculty who is knowledgeable in computational methods that is willing to mentor me in this regard. I have experimented a little bit with Quantum Espresso software packages but am having trouble getting good direction for how to get to the level that I'd like to be at for my experiments

For context: my bachelor's degree was in Chemistry where I did some projects in computational chemistry with Gaussian software, calculating transition state energies for organic synthesis. My honours project was in organometallics with no computational aspect (I would have liked to incorporate it into the project but time limitations prevented that). My current topic is a bit of a side-step from my previous studies but I would really like to be successful and knowledgeable in this field.

TLDR; Me do solid-state battery research, me want use DFT/MD simulations for battery development. Me not know best options, have used Quantum Espresso and Gaussian. Want know best way to proceed. Free/cheap options are preferred.

Thank you

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u/Solid_State_Mate — 10 days ago

Rotation of η2-Ligands around their Metal-Ligand bond axis

Hey everyone, I am working on a bit of a hobby project with orca. I want to investigate the rotational barrier of a η2-ethylene Ligand on an Fe(0) center. The whole complex is similar to Fe(CO)3(η2-C2H4)2 (ethylenes are substituted with COOMe in my model).

I have tried to perform relaxed potential surface scans along various dihedral angles with and without the help of Dummy atoms in order to get the correct rotation around the η2 coordination axis.

However nothing I tried yielded the desired outcome. It is always just the dummy atom slipping out of position or something similar, so I am asking here, if any of you have ever encountered a similar problem or whether you have any pointers for things to try.

Cheers and thanks for reading my essay😅

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u/schelias — 8 days ago

Htf do you express diborane with SMILES?

Diborane has an odd case of four electrons making up four bonds in its center. But in SMILES there's no such thing as a half bond, only the aromatic one which counts as one and a half.

The python library RDKit wont budge because of this. Any suggestions?

u/RedstoneGG4 — 10 days ago

Open-source torsional Monte Carlo conformer generator

Hey all! I've been lurking for a while, but this is my first post. I wanted to share a recent project called openconf. It's a conformer generator that quickly produces diverse conformational ensembles for traditional organic small molecules, inorganic and organometallic compounds, and macrocycles.

We built it because we wanted something faster than repeatedly running the RDKit's ETKDG to generate diverse conformer ensembles across a wide range of chemistries. ETKDG's rule-based conformer building is great, but repeated runs often produce very similar conformers. To complement this, we added a set of Monte Carlo moves to quickly explore more diverse regions of conformational space. Combined with iterative pruning and a few other ideas, we've been pretty happy with the results.

Feedback from the community is very welcome. If you're doing something with conformer generation, I'd love to hear how openconf works for you. It's open source, MIT licensed, and has already received contributions from outside Rowan.

Check it out here: https://github.com/rowansci/openconf.

u/endbring3r — 11 days ago

What would be a great (computational) library that you would like to see

What would you like someone would write? What tools would you like to see in it?

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u/rdguez — 12 days ago