Inferring the Isotropic-Nematic Phase Transition with Generative Machine Learning

Beyerle, Eric R.; Tiwary, Pratyush

doi:10.1103/1wdj-ym3s

Inferring the Isotropic-Nematic Phase Transition with Generative Machine Learning

Journal Article · Wed Aug 06 00:00:00 EDT 2025 · Physical Review Letters

DOI:https://doi.org/10.1103/1wdj-ym3s· OSTI ID:3005751

Beyerle, Eric R. ^[1]; ^[1]

University of Maryland, College Park, MD (United States)

Generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. We utilize a score-based modeling procedure called thermodynamic maps to describe the isotropic-nematic phase transition in a melt of Gay-Berne ellipsoids. When trained on samples from a single temperature on either side of the phase transition, this generative machine learning approach infers effectively the nematic order parameter at intermediate temperatures. Furthermore, these results demonstrate score-based models’ ability to learn the physics of a nontrivial liquid crystal phase transition.

View Accepted Manuscript (DOE)

Research Organization:: University of Maryland, College Park, MD (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0021009

OSTI ID:: 3005751

Alternate ID(s):: OSTI ID: 3003390

Journal Information:: Physical Review Letters, Journal Name: Physical Review Letters Journal Issue: 6 Vol. 135; ISSN 1079-7114; ISSN 0031-9007

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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