“CrystalNets: a web app for topology determination of crystalline structures”, by Lionel Zoubritzky, now published in Comput. Phys. Commun. #chemsky #compchem www.sciencedirect.com/science/arti...

With some delay today is a #tbt of latest #JournalClub entries from our group. First off, understanding and evaluating #uncertainty in #MLIP by @clarakirkvold.bsky.social www.grynova-ccc.org/journal-club...

Solvent-Inclusive ML/MM Simulations: Assessments of Structural, Dynamical, and Thermodynamic Accuracy Chemical reactions in solution are central to biological function, synthetic chemistry, and materials design. Accurate modeling of these systems is essential for obtaining mechanistic insights but remains computationally demanding. Hybrid machine-learned/molecular mechanics (ML/MM) simulations offer a promising compromise between quantum accuracy and computational efficiency. However, most existing ML/MM methods exclude solvent molecules from the ML region, limiting their ability to capture solvent-mediated reactivity. In this study, we introduce a solvent-inclusive ML/MM methodology that addresses this limitation. Our approach defines the ML and MM regions using a fixed spatial boundary, avoiding costly topology updates, and handles interactions across the boundary using a force-partitioning scheme. We evaluate our framework through simulations of bulk water and the solvent-mediated dissociation of formic acid using different ML region sizes. Our results reveal that structural and dynamical properties of bulk water are preserved for sufficiently large ML regions. Notably, we show that the choice of the potential energy surface representation for the ML and MM regions can affect these properties, especially at smaller ML region sizes. Our calculations of the free energy profiles for formic acid dissociation also show trends consistent with reference systems, but deviations are observed. We comment on potential reasons for these observations. Overall, our study highlights the promise of ML/MM approaches that are solvent-inclusive and provides directions for further development.

Proud to be a coauthor on this work led by Varun Gopal and Sapna Sarupria! In this work, we introduce a solvent-inclusive ML/MM methodology that can capture solvent-meditated reactivity.

Thanks to all the authors on this project!

Check out our work in JCIM: pubs.acs.org/doi/10.1021/...

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Privileged to organise (with @clarakirkvold.bsky.social, @jeanquertinmont.bsky.social and Neha Yadav) the inaugural @uobchemistry.bsky.social conference!
Featuring: fantastic presentations from UoB postdocs, keynotes from Rachel O'Reilly and @atarzia.bsky.social, careers panel, and a @rsc.org talk.

Had a great time at #IMRC33, last week! I got to present my work on developing topological representations for nanoporous materials (preprint coming soon) and really enjoyed hearing about everyone’s research and chatting science with so many people!

Excellent talk by @clarakirkvold.bsky.social at #IMRC33 on our PHuN new representation for #ML on ordered #materials #MOFs #COFs, preprint coming very soon!

Happy to have just attended the #XAI world conference in Istanbul. Thanks to everyone for the excellent presentations, informative posters, and interesting discussions!

Trump Has Cut Science Funding to Its Lowest Level in Decades The lag in funding extends far beyond D.E.I. initiatives, affecting almost every area of science: chemistry, computing, engineering, materials and more.

I know there is so much bad stuff happening at once it's easy to look the other way at the decimation of science in the US, but the damage this will cause to the US will be felt for decades, maybe generations.

www.nytimes.com/interactive/...

In today’s #good_practices #Journal_Club @clarakirkvold.bsky.social discusses the #FAIR #data principles and their implementations in #chemistry www.grynova-ccc.org/journal-club...

@clarakirkvold.bsky.social presents her exciting work on a compact topological representation for reticular materials at #AI4AM; paper is in prep.! #ML #COF #MOF