近日，美国马里兰大学的Pratyush Tiwary及其研究团队取得一项新进展。经过不懈努力，他们
在这项工作中，研究人员开发了一种方法，能够基于对不同相稳定域内极少量的观测数据，来表征相变的通用属性。研究人员称这种方法为热力学图（Thermodynamic Maps，简称TM），它结合了统计力学、分子模拟以及基于评分的生成模型。
热力学图使研究人员能够在广泛的温度范围内学习任意热力学观测值的温度依赖性。研究人员通过计算相变属性（如熔点、温度依赖的热容和临界指数）来展示其实用性。例如，研究人员证明热力学图能够推断出伊辛模型的铁磁相变，包括温度依赖的热容和临界指数，尽管他们从未见过来自相变域的样本。
此外，他们还高效地表征了温度依赖的构象集合，并计算了两个RNA系统（一个是GCAA四环结构，另一个是HIV-TAR RNA）的熔化曲线，这两个系统由于具有类似玻璃的能量景观而难以采样。
据悉，相变在生命中无处不在，但却难以准确量化和描述。
附：英文原文
Title: Inferring phase transitions and critical exponents from limited observations with thermodynamic maps
Author: Herron, Lukas, Mondal, Kinjal, Schneekloth, John S., Tiwary, Pratyush
Issue&Volume: 2024-12-16
Abstract: 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.
DOI: 10.1073/pnas.2321971121
Source: https://www.pnas.org/doi/abs/10.1073/pnas.2321971121