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Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems

Journal of applied physics, 2023-01, Vol.133 (1)

Seko, Atsuto

2023

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On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity

The Journal of Chemical Physics, 2024-06, Vol.160 (21)

2024

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Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics

Computational materials science, 2022-03, Vol.204, p.111137, Article 111137

2022

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Representation of compounds for machine-learning prediction of physical properties

Physical review. B, 2017-04, Vol.95 (14), p.144110, Article 144110

2017

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Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

Physical review. B, 2016-03, Vol.93 (11), Article 115104

2016

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Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power

The Journal of chemical physics, 2018-06, Vol.148 (23), p.234106-234106

2018

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Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides

The Journal of chemical physics, 2021-06, Vol.154 (22), p.224117-224117

2021

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Compositional descriptor-based recommender system for the materials discovery

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241719-241719

2018

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Application of machine learning potentials to predict grain boundary properties in fcc elemental metals

Physical review materials, 2020-12, Vol.4 (12), Article 123607

2020

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Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

Physical review materials, 2017-11, Vol.1 (6), Article 063801

2017

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Recommender System of Successful Processing Conditions for New Compounds Based on a Parallel Experimental Data Set

Chemistry of materials, 2019-12, Vol.31 (24), p.9984-9992

2019

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Global structure optimization following imaginary phonon modes accelerated by machine learning potentials in Cu, Ag, and Au

Journal of the Ceramic Society of Japan, 2023-10, Vol.131 (10), p.746-750, Article 23056

2023

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Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system

Journal of the Ceramic Society of Japan, 2023-10, Vol.131 (10), p.762-766, Article 23053

2023

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Structure and stability of a homologous series of tin oxides

Physical review letters, 2008-02, Vol.100 (4), p.045702-045702, Article 045702

2008

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Progress in nanoinformatics and informational materials science

MRS bulletin, 2018-09, Vol.43 (9), p.690-695

2018

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Fast material search of lithium ion conducting oxides using a recommender system

Journal of materials chemistry. A, Materials for energy and sustainability, 2020-01, Vol.8 (23), p.11582-11588

2020

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Native defects in oxide semiconductors: a density functional approach

Journal of physics. Condensed matter, 2010-09, Vol.22 (38), p.384211

2010

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Enumeration of nonequivalent substitutional structures using advanced data structure of binary decision diagram

The Journal of chemical physics, 2020-09, Vol.153 (10), p.104109-104109

2020

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Mode decomposition based on crystallographic symmetry in the band-unfolding method

Physical review. B, 2017-01, Vol.95 (2), p.024305, Article 024305

2017

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Recommender system for discovery of inorganic compounds

npj computational materials, 2022-10, Vol.8 (1), p.1-7, Article 217

2022

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Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides

Physical review. B, 2016-02, Vol.93 (5), Article 054112

2016

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Exploring a potential energy surface by machine learning for characterizing atomic transport

Physical review. B, 2018-03, Vol.97 (12), Article 125124

2018

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First-principles interatomic potentials for ten elemental metals via compressed sensing

Physical review. B, Condensed matter and materials physics, 2015-08, Vol.92 (5), Article 054113

2015

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Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

Physical review. B, Condensed matter and materials physics, 2014-02, Vol.89 (5), Article 054303

2014

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Phonon softening in paramagnetic bcc Fe and its relationship to the pressure-induced phase transition

Physical review. B, Condensed matter and materials physics, 2014-10, Vol.90 (13), Article 134106

2014

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Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems

On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity

Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics

Representation of compounds for machine-learning prediction of physical properties

Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power

Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides

Compositional descriptor-based recommender system for the materials discovery

Application of machine learning potentials to predict grain boundary properties in fcc elemental metals

Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

Recommender System of Successful Processing Conditions for New Compounds Based on a Parallel Experimental Data Set

Global structure optimization following imaginary phonon modes accelerated by machine learning potentials in Cu, Ag, and Au

Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system

Structure and stability of a homologous series of tin oxides

Progress in nanoinformatics and informational materials science

Fast material search of lithium ion conducting oxides using a recommender system

Native defects in oxide semiconductors: a density functional approach

Enumeration of nonequivalent substitutional structures using advanced data structure of binary decision diagram

Mode decomposition based on crystallographic symmetry in the band-unfolding method

Recommender system for discovery of inorganic compounds

Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides

Exploring a potential energy surface by machine learning for characterizing atomic transport

First-principles interatomic potentials for ten elemental metals via compressed sensing

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

Phonon softening in paramagnetic bcc Fe and its relationship to the pressure-induced phase transition

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