发表机构
National Institute for Materials Science; CNRS-Saint-Gobain-NIMS, IRL 3629（物质材料研究机构; 法国国家科学研究中心-圣戈班-物质材料研究机构，国际联合研究室3629）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于热力学势的可微分相平衡损失函数，结合PyTorch自动微分，实现兼容机器学习的CALPHAD型热力学模型优化，可高效优化多参数体系及原子势模型。
AI 中文摘要
为精确确定相边界与相变，描述各相自由能的热力学模型常需基于实验观测的相平衡进行优化。尽管存在多种热力学优化方法，但这些方法的实现通常与机器学习工作流不兼容，后者要求损失函数可微分计算。本研究推导了一种基于热力学势的相平衡损失函数，该函数可高效计算，并支持在PyTorch框架中通过自动微分实现基于梯度的优化。通过最小化该损失函数，可针对实验相平衡数据优化通用热力学模型参数。采用相图计算（CALPHAD）框架中的热力学模型，我们在不同体系（包括含100余个参数的三元体系）中展示了成功且高效的优化效果。由于该损失函数的定义独立于热力学模型的细节，它可用于通用机器学习热力学模型的优化；特别地，我们展示了基于目标相平衡的自上而下原子势优化。
英文摘要
To accurately determine phase boundaries and phase transitions, thermodynamic models that describe phase free energies often have to be optimized based on experimentally observed phase equilibria. While different approaches exist for thermodynamic optimization, they are often implemented in ways that are not compatible with machine learning workflows requiring differentiable calculation of the loss function. In this work, we derive a phase-equilibrium loss function based on thermodynamic potentials that can be efficiently evaluated and enables gradient-based optimization by auto-differentiation in the \texttt{PyTorch} package. By minimizing this loss function, general thermodynamic model parameters can be optimized with respect to experimental phase-equilibria data. Using thermodynamic models in the CALculation of PHAse Diagram (CALPHAD) framework, we illustrate successful and efficient optimization in different systems, including ternary systems with more than 100 parameters. As the loss function is defined independently of the details of the thermodynamic models, it can be used to optimize machine learning thermodynamic models in general. In particular, we demonstrate top-down optimization of an atomistic potential from target phase equilibria.
