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Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning
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AI
DOI：10.1038/s41524-024-01394-4.png)
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摘要
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.
Keyword:
PLASTIC-DEFORMATION
CORE STRUCTURE
DYNAMICS
MOTION
PLANES
GLIDE
期刊
IF:
11.9
论文数:
2.4K
被引数:
1.7W
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Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential高斯近似势预测的体心立方Fe中的螺位错结构和迁移率
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