activity
20152022
most citedQuantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning

89 citations · 230 across the 9 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2022★ 1 cited

interface_master: Python package building CSL and approximate CSL interfaces of any two lattices -- an effective tool for interface engineers

Yaoshu Xie, Kiyou Shibata, Teruyasu Mizoguchi

Crystalline interfaces are of highly importance in many practical applications. To conduct effective simulation and analysis for coincident site lattice (CSL) interfaces, effective…

cond-mat.mtrl-sci2021★ 6 cited

Accurate Prediction of Bonding Properties by A Machine Learning-based Model using Isolated States Before Bonding

Eiki Suzuki, Kiyou Shibata, Teruyasu Mizoguchi

Given the strong dependence of material structure and properties on the length and strength of constituent bonds and the fact that surface adsorption and chemical reactions are ini…

cond-mat.mtrl-sci2021

Robotic fabrication of high-quality lamellae for aberration-corrected transmission electron microscopy

Hideyo Tsurusawa, Nobuto Nakanishi, Kayoko Kawano +3

Aberration-corrected scanning transmission electron microscopy (STEM) is widely used for atomic-level imaging of materials. To accelerate the discovery of new materials based on at…

cs.LG2020★ 5 cited

On the equivalence of molecular graph convolution and molecular wave function with poor basis set

Masashi Tsubaki, Teruyasu Mizoguchi

In this study, we demonstrate that the linear combination of atomic orbitals (LCAO), an approximation of quantum physics introduced by Pauling and Lennard-Jones in the 1920s, corre…

physics.chem-ph2020★ 89 cited

Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning

Masashi Tsubaki, Teruyasu Mizoguchi

Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT). Although this predict…

cond-mat.mtrl-sci2020

Local thickness and composition measurements from scanning convergent-beam electron diffraction of a binary non-crystalline material obtained by a pixelated detector

K. Nakazawa, K. Mitsuishi, K. Shibata +2

We measured the local composition and thickness of SiO2-based glass material from diffraction. By using four dimensional scanning transmission electron microscopy (4D-STEM), we obt…