The content discusses the challenges in predicting material strength, particularly the Peierls stress, due to computational costs and limitations of empirical force fields. The proposed physics-transfer framework leverages neural networks to learn mappings between characteristic material parameters and accurately predict Peierls stress with high efficiency. By integrating mesoscale physics into computational databases, this approach offers a promising solution for high-throughput materials screening and discovery.
The strength of materials is intricately linked to concepts like Peierls stress in crystal plasticity, which poses challenges due to computational complexities. Empirical atomistic simulations have revolutionized material research but are limited in predicting properties beyond equilibrium states. The development of a physics-transfer framework bridges the gap between low-fidelity force field models and chemically accurate first-principles methods.
By training neural networks on atomistic simulation datasets, the PT framework effectively predicts Peierls stress with high accuracy and efficiency. Uncertainty quantification reveals that PT predictions eliminate physical and system uncertainties, showcasing its potential for accurate material strength screening. The integration of mesoscale physics enhances the predictive capabilities of the framework, offering a comprehensive solution for materials science research.
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소스 콘텐츠 기반
arxiv.org
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