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A general-purpose machine-learning force field for bulk and nanostructured phosphorus

Nature communications, 2020-10, Vol.11 (1), p.5461-5461, Article 5461

2020

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Physics-Inspired Structural Representations for Molecules and Materials

Chemical reviews, 2021-08, Vol.121 (16), p.9759-9815

2021

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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Advanced materials (Weinheim), 2019-11, Vol.31 (46), p.e1902765-n/a

2019

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Machine learning based interatomic potential for amorphous carbon

Physical review. B, 2017-03, Vol.95 (9), p.94203, Article 094203

2017

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Machine Learning a General-Purpose Interatomic Potential for Silicon

Physical review. X, 2018-12, Vol.8 (4), p.041048, Article 041048

2018

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Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

Chemistry of materials, 2018-11, Vol.30 (21), p.7446-7455

2018

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Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

The journal of physical chemistry letters, 2018-06, Vol.9 (11), p.2879-2885

2018

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On representing chemical environments

Physical review. B, Condensed matter and materials physics, 2013-05, Vol.87 (18), Article 184115

2013

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Accuracy and transferability of Gaussian approximation potential models for tungsten

Physical review. B, Condensed matter and materials physics, 2014-09, Vol.90 (10), Article 104108

2014

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De novo exploration and self-guided learning of potential-energy surfaces

npj computational materials, 2019-10, Vol.5 (1), p.1-9, Article 99

2019

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Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

npj computational materials, 2020-07, Vol.6 (1), Article 104

2020

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Development of a machine learning potential for graphene

Physical review. B, 2018-02, Vol.97 (5), Article 054303

2018

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Machine-learned interatomic potentials for alloys and alloy phase diagrams

npj computational materials, 2021-01, Vol.7 (1), p.1-9, Article 24

2021

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Structural Dynamics Descriptors for Metal Halide Perovskites

Journal of physical chemistry. C, 2023-09, Vol.127 (38), p.19141-19151

2023

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Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

The Journal of chemical physics, 2023-07, Vol.159 (4)

2023

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Nested sampling for materials

The European physical journal. B, Condensed matter physics, 2021-08, Vol.94 (8), Article 159

2021

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Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

Physical review. B, Condensed matter and materials physics, 2013-08, Vol.88 (5), Article 054104

2013

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Similarity Between Amorphous and Crystalline Phases: The Case of TiO2

The journal of physical chemistry letters, 2018-06, Vol.9 (11), p.2985-2990

2018

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Machine-learned multi-system surrogate models for materials prediction

npj computational materials, 2019-04, Vol.5 (1), Article 51

2019

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Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

Physical review materials, 2018-01, Vol.2 (1), Article 013808

2018

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Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

npj computational materials, 2021-06, Vol.7 (1), p.1-12, Article 97

2021

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Learning the electronic density of states in condensed matter

Physical review. B, 2020-12, Vol.102 (23), p.1, Article 235130

2020

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Hydrogen induced fast-fracture

Journal of the mechanics and physics of solids, 2020-01, Vol.134, p.103740, Article 103740

2020

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Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon

Physical review. B, 2020-11, Vol.102 (17), p.174201, Article 174201

2020

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Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

npj computational materials, 2018-12, Vol.4 (1), p.1-7, Article 69

2018

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A general-purpose machine-learning force field for bulk and nanostructured phosphorus

Physics-Inspired Structural Representations for Molecules and Materials

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Machine learning based interatomic potential for amorphous carbon

Machine Learning a General-Purpose Interatomic Potential for Silicon

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

On representing chemical environments

Accuracy and transferability of Gaussian approximation potential models for tungsten

De novo exploration and self-guided learning of potential-energy surfaces

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

Development of a machine learning potential for graphene

Machine-learned interatomic potentials for alloys and alloy phase diagrams

Structural Dynamics Descriptors for Metal Halide Perovskites

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

Nested sampling for materials

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

Similarity Between Amorphous and Crystalline Phases: The Case of TiO2

Machine-learned multi-system surrogate models for materials prediction

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

Learning the electronic density of states in condensed matter

Hydrogen induced fast-fracture

Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

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