13 citations · 16 across the 3 of their papers we have counts for
4 papers
An atom-based machine-learned dipole-moment model and application to conjugated systems
Yuansheng Zhao, Tamio Yamazaki, Ryohei Hosoya +2
We introduce a method for predicting the dipole moments of molecular systems by systematically decomposing the total dipole moment into the sum of atomic contributions using the wa…
Overcoming the Matrix-Product-State Encoding Barrier via DMRG-Guided Probabilistic Imaginary-Time Evolution
Masari Watanabe, Hirofumi Nishi, Taichi Kosugi +2
Ground-state preparation is a fundamental task in quantum simulation, because the overlap of the prepared state with the true ground state significantly affects the overall cost of…
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…