5 papers
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…
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…
Active Learning of a Neural Network Potential for Large-Scale Atomistic Simulations of Polymer Electrolyte Membranes
Yuta Yoshimoto, Naoki Matsumura, Meguru Yamazaki +3
Machine learning interatomic potentials (MLIPs) can achieve near density-functional-theory (DFT) accuracy at force-field computational cost; however, long-time, large-scale molecul…
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…
Transferability of the chemical bond-based machine learning model for dipole moment: the GHz to THz dielectric properties of liquid propylene glycol and polypropylene glycol
Tomohito Amano, Tamio Yamazaki, Naoki Matsumura +2
We conducted a first-principles study of the dielectric properties of liquid propylene glycol (PG) and polypropylene glycol (PPG) using a recently developed chemical bond-based mac…