Heather J. Kulik — Papers on Cortexa

arxivphysics.chem-phcs.LGq-bio.BM2026-07-01

Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases

Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demanding. Neural network potentials (NNPs) offer a promising route to reduce this cost, but enzymes present challenges beyond small mol…

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arxivphysics.chem-phcs.LG2026-06-29

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes Kästner, Heather J. Kulik

Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species. This work introduces ElemeNet…

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