Discovering High-Strength Alloys via Physics-Transfer Learning
This work addresses the accuracy-efficiency dilemma in materials science for predicting alloy strength, enabling the discovery of new high-strength materials.
The researchers tackled the challenge of predicting material strength (Peierls stress) in alloys, which is computationally intensive, by developing a physics-transfer learning framework that uses neural networks trained on force field simulations to screen high-strength binary alloys with chemical accuracy, leading to the fabrication of previously unexplored alloys.
Predicting the strength of materials requires considering various length and time scales, striking a balance between accuracy and efficiency. Peierls stress measures material strength by evaluating dislocation resistance to plastic flow, reliant on elastic lattice responses and crystal slip energy landscape. Computational challenges due to the non-local and non-equilibrium nature of dislocations prohibit Peierls stress evaluation from state-of-the-art material databases. We propose a data-driven framework that leverages neural networks trained on force field simulations to understand crystal plasticity physics, predicting Peierls stress from material parameters derived via density functional theory computations, which are otherwise computationally intensive for direct dislocation modeling. This physics transfer approach successfully screen the strength of metallic alloys from a limited number of single-point calculations with chemical accuracy. Guided by these predictions, we fabricate high-strength binary alloys previously unexplored, utilizing high-throughput ion beam deposition techniques. The framework extends to problems facing the accuracy-performance dilemma in general by harnessing the hierarchy of physics of multiscale models in materials sciences.