Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

位错迁移率决定了位错对施加应力的响应，是控制塑性变形演化的晶体材料的基本属性。推导迁移率定律的传统方法依赖于基础物理的唯象模型，其自由参数反过来适合不同温度和应力条件下的少量直觉驱动的原子尺度模拟。对于对应力、温度和局部环境具有复杂依赖性的材料（例如体心立方晶体 (BCC) 金属和合金）来说，这种繁琐且耗时的方法变得尤其麻烦。在本文中，我们提出了一种新颖的、不确定性量化驱动的主动学习范式，用于使用具有物理信息架构的图神经网络（GNN）从自动化高通量大规模分子动力学模拟中学习位错迁移率定律。我们证明，与 BCC 金属中现有的唯象迁移率定律相比，这种基于物理的图神经网络 (PI-GNN) 框架能够更准确地捕捉底层物理现象。