Tsuyoshi Miyazaki

University of Tsukuba

Japan

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Articles (11)

A simple yet reliable scheme for reducing the dimensionality of atomic descriptors is proposed to construct the machine-learning forces for stable and accurate MD simulations.

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2023

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2022

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2022

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2021

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2021

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2020

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2020

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2020

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2020

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2020

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2020

Collaborators (4)

Sergiu Arapan

VSB - Technical University of Ostrava

CZECH REPUBLIC

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Hiroaki Nakamura

Prof. Ph. D

National Institute for Fusion Science

JAPAN

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Lionel Truflandier

University of Bordeaux

FRANCE

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David Bowler

University College London

UNITED KINGDOM

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Tsuyoshi Miyazaki

Research Interests

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Articles (11)

Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces

Large-Scale DFT Methods for Calculations of Materials with Complex Structures

Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

Structural change of damaged polyethylene by beta-decay of substituted tritium using reactive force field

The investigation of methane storage at the Ni-MOF-74 material: a periodic DFT calculation

Large scale and linear scaling DFT with the CONQUEST code

Blue moon ensemble simulation of aquation free energy profiles applied to mono and bifunctional platinum anticancer drugs

The pseudoatomic orbital basis: electronic accuracy and soft-mode distortions in ABO3 perovskites

First-principles study of topologically protected vortices and ferroelectric domain walls in hexagonal YGaO3

First-principles study of the charge ordered phase in κ−D3(Cat-EDT-TTF/ST)2 : Stability of π -electron deuterium coupled ordering in hydrogen-bonded molecular conductors

Molecular dynamics study on DNA damage by tritium disintegration

Collaborators (4)

Sergiu Arapan

Hiroaki Nakamura

Lionel Truflandier

David Bowler

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