Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

TL;DR

This paper introduces a physics-informed machine learning framework using Graph Neural Networks to accurately model dislocation mobility in crystalline materials, reducing reliance on phenomenological models and enabling high-throughput simulations.

Contribution

The authors develop a novel active learning approach with physics-informed GNNs to learn dislocation mobility laws directly from molecular dynamics data, improving accuracy over traditional models.

Findings

PI-GNN captures physics more accurately than phenomenological laws

Active learning reduces the need for extensive manual parameter fitting

Framework enables high-throughput, automated mobility law derivation

Abstract

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.