2 papers
cond-mat.mtrl-sci2022
Convergence Acceleration in Machine Learning Potentials for Atomistic Simulations
Dylan Bayerl, Christopher M. Andolina, Shyam Dwaraknath +1
Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional…
cond-mat.mtrl-sci2020
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…