most citedOptimization and Validation of a Deep Learning CuZr Atomistic Potential: Robust Applications for Crystalline and Amorphous Phases with near-DFT Accuracy

48 citations · 48 across the 1 of their papers we have counts for

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1 paper

cond-mat.mtrl-sci2020★ 48 cited

Optimization and Validation of a Deep Learning CuZr Atomistic Potential: Robust Applications for Crystalline and Amorphous Phases with near-DFT Accuracy

Christopher M. Andolina, Philip Williamson, Wissam A. Saidi

We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu-Zr materials, an example of a binary alloy sys…