most citedGenerator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations

17 citations · 17 across the 1 of their papers we have counts for

collaborators

1 paper

cond-mat.mtrl-sci2024★ 17 cited

Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations

Naoki Matsumura, Yuta Yoshimoto, Tamio Yamazaki +5

Neural network potentials (NNPs) enable large-scale molecular dynamics (MD) simulations of systems containing >10,000 atoms with the accuracy comparable to ab initio methods and pl…