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cond-mat.stat-mech2024
Inferring the Isotropic-nematic Phase Transition with Generative Machine Learning
Eric R. Beyerle, Pratyush Tiwary
Contemporary work implies generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. In this Letter, we util…
physics.bio-ph2024
An Information Bottleneck Approach for Markov Model Construction
Dedi Wang, Yunrui Qiu, Eric Beyerle +2
Markov state models (MSMs) are valuable for studying dynamics of protein conformational changes via statistical analysis of molecular dynamics (MD) simulations. In MSMs, the comple…