[论文解读] Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning

Patrick G. Sahrmann, Benjamin Nebgen+2 more|arXiv (Cornell University)|Feb 26, 2026

Machine Learning in Materials Science被引用 0

一句话总结

该论文通过训练势的平均力的热响应力，提出了一种数据高效的方法将温度依赖性嵌入到机器学习的粗粒化力场中，从而提高在不同热力学状态下的可迁移性。

ABSTRACT

Machine-learned (ML) coarse-grained (CG) models are a promising tool for significantly enhancing the efficiency of molecular simulations by systematically removing degrees of freedom while retaining fidelity to the underlying fine-grained model. The CG potential of mean force (PMF) is inherently dependent on thermodynamic conditions and, hence, a CG force-field (FF) which is trained at one thermodynamic state point is not necessarily accurate at another. We propose, in this work, a novel and data-efficient means of learning temperature dependence into ML CG force-fields via training on the thermal response forces of the PMF. We demonstrate how incorporating these terms into ML CG FFs confers significantly improved transferability for CG water models and demonstrate how this transferability enables accurate and predictive CG dynamics.

研究动机与目标

• 动机：提高ML粗粒化力场在不同温度下的热力学可迁移性。
• 目标：通过热响应力将温度依赖性引入CG力场。
• 目的：在变化热力学条件下实现准确、可预测的CG动力学。

提出的方法

• 使用来自PMF的热响应力来训练ML CG力场。
• 在CG力场中加入热响应项以编码温度依赖性。
• 展示CG水模型在不同热力学状态下的可迁移性提升。

实验结果

研究问题

• RQ1 能否从PMF数据中学习热响应力以编码CG模型的温度依赖性？
• RQ2 包含热响应力是否能提高ML CG力场对不同温度的可迁移性？
• RQ3 这些项对CG动力学的准确性和预测能力有何影响？

主要发现

• 将热响应力引入ML CG力场可显著提升CG水模型在不同温度下的可迁移性。
• 增强的可迁移性使在变化热力学条件下的CG动力学更为准确和具有预测性。