One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications.

翻译：人工智能（AI）最具前景的应用之一是基于先前积累的数据，结合已知物理原理（包括对称性和守恒定律）所提供的约束，实现自动化科学发现。这种自动化假设生成与验证能够帮助科学家研究传统物理直觉可能失效的复杂现象。基于广义昂萨格原理，我们开发了一个平台，可直接从随机耗散系统的微观轨迹观测中学习其宏观动力学描述。该方法能够同时构建约化热力学坐标，并解释这些坐标上的动力学行为。我们通过理论研究和实验验证了在外部场作用下长聚合物链的拉伸过程，证明了该方法的有效性：具体而言，我们学习到了三个可解释的热力学坐标，构建了聚合物拉伸的动力学景观（包括稳定态与过渡态的识别以及拉伸速率的调控）。本文提出的通用方法可应用于广泛的科学与技术领域。