29 citations · 62 across the 4 of their papers we have counts for
4 papers
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…
A chemical bond-based machine learning model for dipole moment: Application to dielectric properties of liquid methanol and ethanol
Tomohito Amano, Tamio Yamazaki, Shinji Tsuneyuki
We introduce a versatile machine-learning scheme for predicting dipole moments of molecular liquids to study dielectric properties. We attribute the center of mass of Wannier funct…
Lattice dielectric properties of rutile : First-principles anharmonic self-consistent phonon study
Tomohito Amano, Tamio Yamazaki, Ryosuke Akashi +2
We calculate the lattice dielectric function of strongly anharmonic rutile from ab initio anharmonic lattice dynamics methods. Since an accurate calculation of the…