Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

doi:10.1038/s41524-024-01394-4

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

2024-09-14

1

OA

AI

DOI：10.1038/s41524-024-01394-4

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

PLASTIC-DEFORMATION

CORE STRUCTURE

DYNAMICS

MOTION

PLANES

GLIDE

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Abstract

Journal

Organization