Inferring phase transitions and critical exponents from limited observations with thermodynamic maps

• 成果类型: Article
• 署名作者: Herron, Lukas; Mondal, Kinjal; Schneekloth, John S.; Tiwary, Pratyush
• 署名单位: University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); University System of Maryland; University of Maryland College Park
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14596
• DOI: 10.1073/pnas.2321971121
• 发表日期: 2024-12-24
• 关键词: force-field; rna; MODEL; state

摘要

Phase transitions are ubiquitous across life, yet hard to quantify and describe accurately. In this work, we develop an approach for characterizing generic attributes of phase transitions from very limited observations made deep within different phases' domains of stability. Our approach is called thermodynamic maps (TM), which combines statistical mechanics and molecular simulations with score-based generative models. TM enable learning the temperature dependence of arbitrary thermodynamic observables across a wide range of temperatures. We show its usefulness by calculating phase transition attributes such as melting temperature, temperature-dependent heat capacities, and critical exponents. For instance, we demonstrate the ability of TM to infer the ferromagnetic phase transition of the Ising model, including temperature dependent heat capacity and critical exponents, despite never having seen samples from the transition region. In addition, we efficiently characterize the temperature dependent conformational ensemble and compute melting curves of the two RNA systems: a GCAA tetraloop and the HIV-TAR RNA, which are notoriously hard to sample due to glassy-like energy landscapes.