3 papers
cond-mat.mtrl-sci2026
Generative Models for Crystalline Materials
Houssam Metni, Laura Ruple, Lauren N. Walters +13
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has eme…
cs.LG2025
Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
Marlen Neubert, Patrick Reiser, Frauke Gräter +1
Hydrogen atom transfer (HAT) reactions are essential in many biological processes, such as radical migration in damaged proteins, but their mechanistic pathways remain incompletely…
cs.LG2024
PAL -- Parallel active learning for machine-learned potentials
Chen Zhou, Marlen Neubert, Yuri Koide +5
Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively e…