Mark A. Ratner Postdoctoral Scholar at Northwestern Automated Computational Methods, Heterogeneous Catalysis, and Machine Learning Interatomic Potentials

Tuscaloosa, Alabama
Joined June 2020
I am happy to announce that I was awarded the 2022 DOE CSGF to continue my studies at @UofAlabama with Dr. Szilvási (@SzilvasiGroup). It is also an honor to be the first student pursuing a Ph.D. within the state of Alabama to receive this award.
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Tristan Maxson retweeted
Defect simulations are notoriously sensitive to the many choices required 👨‍💻📊 In this Perspective, Alex Squires @lonepair @scanlond81 and I highlight best practices in calculating 𝘢𝘯𝘥 𝘳𝘦𝘱𝘰𝘳𝘵𝘪𝘯𝘨 point defect properties, hoping to establish guidelines for this field 🌟
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Tristan Maxson retweeted
CO puzzle is a fundamental issue in modeling heterogeneous catalysts. We show that a simple, self-consistent DFT+U scheme applied to the C and O p orbitals restores the experimental site preference, vibrational frequency, and adsorption energies within ±0.1 eV
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Tristan Maxson retweeted
If you are interested in the multi-scale simulation of polymer transformations, we created to tutorial how to apply our MUSIK approach that can provide agreement with experiments without parameter fitting. Congrats, @sophyia__ ! chemrxiv.org/engage/chemrxiv…
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An article I co-authored (with Gbolagade Olajide and @SzilvasiGroup) just came out in @ACSCatalysis. In this work, we show that MLIPs can reveal some cases where atoms appear to disappear under in situ TEM microscopy due to low barriers and thermal motion.
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If you work with experimental in situ TEM for catalysis or as a computational researcher looking to simulate TEM, please take a look at our article. Also special thanks to Toma Susi and the abTEM developers for assistance in setting up the proper simulation parameters.
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Wiley shot a video about our recent @angew_chem Novit paper. Paper is here: onlinelibrary.wiley.com/doi/…
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Thank you @nvidia for supporting my group's research on applying machine learning interatomic potentials in materials science via an #NVIDIAGrant from @NVIDIAAIDev.
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Published in #AngewandteChemieNovit: Global optimization via machine learning interatomic potentials enables identifying more realistic and stable of supported nanoparticles. By Tibor Szilvási & co-workers (@SzilvasiGroup). Read it here: doi.org/10.1002/anov.70008
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Happy to have the my nanoparticle simulation work out in the new Angewante Novit (@angew_chem) open access journal!
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Tristan Maxson retweeted
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Tristan Maxson retweeted
Comp. chem. = Protestantism. All QM codes are incompatible; each hopes for supremacy and pretends the others don't exist. Materials science = Catholicism. All codes work w/ the ASE; everyone privately criticizes the ASE and dreams of a new order but no one wants to break unity.
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Tristan Maxson retweeted
Breaking the fifth wall - social media team is headed to Atlanta for @NAM29NACS ! @JWHarrisCatal @SzilvasiGroup
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New paper is out in Journal of Catalysis. We wrote a comprehensive review on the application of machine learning potentials in heterogeneous catalysis. Congrats Jide, Khagendra, Sophia, and Ademola! sciencedirect.com/science/ar…
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Great work by some of the others in the Szilvasi group on how MLIPs and catalysis interface together and the future directions.
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