3 papers
cs.LG2025
Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations
Naoki Matsumura, Yuta Yoshimoto, Yuto Iwasaki +2
Neural network potentials (NNPs) offer a powerful alternative to traditional force fields for molecular dynamics (MD) simulations. Accurate and stable MD simulations, crucial for e…
cond-mat.mtrl-sci2025
Molecular Dynamics Simulations of SrTiO with Oxygen Vacancies using Neural Network Potentials
Kazutaka Nishiguchi, Ryota Yamamoto, Meguru Yamazaki +4
A precise analysis of point defects in solids requires accurate molecular dynamics (MD) simulations of large-scale systems. However, ab initio MD simulations based on density funct…
cond-mat.mtrl-sci2024
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…