Computational chemistry and machine learning @DEShawResearch | PhD at @HarvardCCB and @mit_dmse 2023 | @uoftphysics 2017 | @queensu 2016

Manhattan, NY
Joined March 2021
Happy for the opportunity to present my latest work done in @RGBLabMIT at #ACSFall2023.
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Rafael Gomez-Bombarelli was one of twenty-six members of the MIT engineering faculty and researchers who received awards in recognition of their scholarship, service, and overall excellence in the past calendar quarter.
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How to optimize the per-site activity of oxide catalysts? Check out our work, "Atom-by-Atom Design of Metal Oxide Catalysts for the Oxygen Evolution Reaction with Machine Learning," led by our new PhD graduate, Jackie, and in collaboration with @EELabMIT! arxiv.org/abs/2305.19930
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Jackie’s defense brought this amazing group of young people and big bright smiles together with @RGBLabMIT @mit_dmse @MITMechE @RLEatMIT
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Use a VAE to predict atomic coordinates from coarse-grained coordinates (backmapping) for any protein system. @SoojungYang2 @RGBLabMIT arxiv.org/abs/2303.01569
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Chemically Transferable Generative Backmapping of Coarse-Grained Proteins. (arXiv:2303.01569v1 [cs.LG]) #machinelearning #compchem cutt.ly/P8Pkyvc
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This weekend some of our group members braved the cold and enjoyed a winter hike at the Fells!❄️
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Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential. NEW #ASAP by @AxelrodSimon, Eugene Shakhnovich, and @RGBLabMIT Read it here: go.acs.org/3NW
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We use differentiable simulations to train machine learned interatomic potentials from radial distribution functions, opening a new avenue to derive force fields from experimental data. @RGBLabMIT @JDietschreit aippub.org/3Y46eRg
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Jean-Philip Piquemal
@jppiquem28 Jan 2023
#compchem Good read: Learning pair potentials using differentiable simulations doi.org/10.1063/5.0126475
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Just out in ACS Central Science! We made an automated workflow for predicting the thermal half-lives of azobenzene derivatives. TL;DR isomerization involves intersystem crossing, multi-reference effects are critical, and ML gives fast and accurate results! pubs.acs.org/doi/full/10.102…
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Our recent work "Design principles for transition metal nitride stability and ammonia generation in acid" is featured #OnTheCover of @Joule_CP. Check out the paper using this free link (valid before 2/7/2023): authors.elsevier.com/a/1gHD0… @EELabMIT @mit_romangroup #Energy #ChemTwitter
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Some minds think alike! Work by Kaupp and @stefanhecht groups pubs.acs.org/doi/pdf/10.1021… in agreement with @AxelrodSimon's preprint arxiv.org/abs/2207.11592. Thermal azobenzene isomerization is all about low-lying triplet! Shoutout to @gagliardi8 and others who called it in 2014
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We're hiring a Research Engineer on the Structured Intelligence team at @DeepMind. If you have expertise in learning simulation, GNNs, generative models, or ML for the sciences, please apply here: boards.greenhouse.io/deepmin…
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𝙷𝚎𝚕𝚕𝚘 𝚆𝚘𝚛𝚕𝚍, 𝚃𝚑𝚎 𝙶𝚘́𝚖𝚎𝚣-𝙱𝚘𝚖𝚋𝚊𝚛𝚎𝚕𝚕𝚒 𝙻𝚊𝚋 𝚑𝚊𝚜 𝚓𝚘𝚒𝚗𝚎𝚍 𝚃𝚠𝚒𝚝𝚝𝚎𝚛! 𝚆𝚎 𝚊𝚛𝚎 𝚎𝚡𝚌𝚒𝚝𝚎𝚍 𝚝𝚘 𝚜𝚑𝚊𝚛𝚎 𝚝𝚑𝚎 𝚠𝚘𝚛𝚔 𝚘𝚏 𝚘𝚞𝚛 𝚐𝚛𝚘𝚞𝚙 𝚖𝚎𝚖𝚋𝚎𝚛𝚜 𝚊𝚗𝚍 𝚝𝚘 𝚎𝚗𝚐𝚊𝚐𝚎 𝚠𝚒𝚝𝚑 𝚝𝚑𝚎 𝚌𝚘𝚖𝚖𝚞𝚗𝚒𝚝𝚢.
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We highlight a recent @NatureComms paper from Rafael Gómez-Bombarelli and colleagues that proposes a neural network approach for speeding up computational screening of photoswitchable molecules, without sacrificing accuracy (rdcu.be/cRZJI). nature.com/articles/s43588-0…
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