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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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Incipient Metals: Functional Materials with a Unique Bonding Mechanism

Advanced materials (Weinheim), 2018-12, Vol.30 (51), p.e1803777-n/a

2018

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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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A Quantum‐Mechanical Map for Bonding and Properties in Solids

Advanced materials (Weinheim), 2019-01, Vol.31 (3), p.e1806280-n/a

2019

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Origins of structural and electronic transitions in disordered silicon

Nature (London), 2021-01, Vol.589 (7840), p.59-64

2021

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Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Advanced materials (Weinheim), 2022-02, Vol.34 (5), p.e2107515-n/a

2022

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Microscopic Complexity in Phase-Change Materials and its Role for Applications

Advanced functional materials, 2015-10, Vol.25 (40), p.6343-6359

2015

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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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Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning

Nature communications, 2024-03, Vol.15 (1), p.1927-1927, Article 1927

2024

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Unraveling Crystallization Mechanisms and Electronic Structure of Phase‐Change Materials by Large‐Scale Ab Initio Simulations

Advanced materials (Weinheim), 2022-03, Vol.34 (11), p.e2109139-n/a

2022

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Surface effects on the crystallization kinetics of amorphous antimony

Nanoscale, 2023-09, Vol.15 (37), p.15259-15267

2023

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X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

Journal of physical chemistry. C, 2021-08, Vol.125 (33), p.18234-18246

2021

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Ab Initio Modeling of α-GeTe(111) Surfaces

Journal of physical chemistry. C, 2012-07, Vol.116 (29), p.15801-15811

2012

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Stabilities and Reconstructions of Clean PbS and PbSe Surfaces: DFT Results and the Role of Dispersion Forces

Journal of physical chemistry. C, 2016-04, Vol.120 (16), p.8813-8820

2016

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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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DFT Studies of Pristine Hexagonal Ge1Sb2Te4(0001), Ge2Sb2Te5(0001), and Ge1Sb4Te7(0001) Surfaces

Journal of physical chemistry. C, 2013-07, Vol.117 (29), p.15075-15089

2013

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Materials Screening for Disorder‐Controlled Chalcogenide Crystals for Phase‐Change Memory Applications

Advanced materials (Weinheim), 2021-03, Vol.33 (9), p.e2006221-n/a

2021

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Sub-Angstrom Characterization of the Structural Origin for High In-Plane Anisotropy in 2D GeS2

ACS nano, 2020-04, Vol.14 (4), p.4456-4462

2020

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Stabilities and Reconstructions of PbTe Crystal Surfaces from Density-Functional Theory

Journal of physical chemistry. C, 2013-11, Vol.117 (46), p.24455-24461

2013

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Many-Body Dispersion Correction Effects on Bulk and Surface Properties of Rutile and Anatase TiO2

Journal of physical chemistry. C, 2016-09, Vol.120 (38), p.21552-21560

2016

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Synthetic pre-training for neural-network interatomic potentials

Machine learning: science and technology, 2024-03, Vol.5 (1), p.15003

2024

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Simulations in the era of exascale computing

Nature reviews. Materials, 2023, Vol.8 (5), p.309-313

2023

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Unconventional two-dimensional germanium dichalcogenides

Nanoscale, 2018-04, Vol.10 (16), p.7363-7368

2018

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Spin Glass Behavior in Amorphous Cr2Ge2Te6 Phase‐Change Alloy

Advanced science, 2023-08, Vol.10 (23), p.n/a

2023

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Data as the next challenge in atomistic machine learning

Nature Computational Science, 2024-06, Vol.4 (6), p.384-387

2024

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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Incipient Metals: Functional Materials with a Unique Bonding Mechanism

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

A Quantum‐Mechanical Map for Bonding and Properties in Solids

Origins of structural and electronic transitions in disordered silicon

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Microscopic Complexity in Phase-Change Materials and its Role for Applications

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

Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning

Unraveling Crystallization Mechanisms and Electronic Structure of Phase‐Change Materials by Large‐Scale Ab Initio Simulations

Surface effects on the crystallization kinetics of amorphous antimony

X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

Ab Initio Modeling of α-GeTe(111) Surfaces

Stabilities and Reconstructions of Clean PbS and PbSe Surfaces: DFT Results and the Role of Dispersion Forces

Similarity Between Amorphous and Crystalline Phases: The Case of TiO2

DFT Studies of Pristine Hexagonal Ge1Sb2Te4(0001), Ge2Sb2Te5(0001), and Ge1Sb4Te7(0001) Surfaces

Materials Screening for Disorder‐Controlled Chalcogenide Crystals for Phase‐Change Memory Applications

Sub-Angstrom Characterization of the Structural Origin for High In-Plane Anisotropy in 2D GeS2

Stabilities and Reconstructions of PbTe Crystal Surfaces from Density-Functional Theory

Many-Body Dispersion Correction Effects on Bulk and Surface Properties of Rutile and Anatase TiO2

Synthetic pre-training for neural-network interatomic potentials

Simulations in the era of exascale computing

Unconventional two-dimensional germanium dichalcogenides

Spin Glass Behavior in Amorphous Cr2Ge2Te6 Phase‐Change Alloy

Data as the next challenge in atomistic machine learning

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