most citedBenchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys

1 citations · 1 across the 1 of their papers we have counts for

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cond-mat.mtrl-sci2026★ 1 cited

Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys

Fei Shuang, Penghua Ying, Kai Liu +5

Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in l…

cond-mat.mtrl-sci2025

Discovering High-Strength Alloys via Physics-Transfer Learning

Yingjie Zhao, Hongbo Zhou, Zian Zhang +4

Predicting the strength of materials requires considering various length and time scales, striking a balance between accuracy and efficiency. Peierls stress measures material stren…