activity
20242026
most citedGenerator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-scale Simulations

17 citations · 20 across the 5 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2026

A Padding Method for Enhanced Encoding of Inorganic Structures with Varying Chemical Compositions

Thang Dang, Haderbache Amir, Tzanakakis Alexandros +1

Designing novel inorganic materials through generative models remains an important challenge for material science, driven by the complexity and diversity of inorganic structures ac…

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-sci2025

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…

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…

physics.chem-ph2024★ 3 cited

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…