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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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LOBSTER: Local orbital projections, atomic charges, and chemical‐bonding analysis from projector‐augmented‐wave‐based density‐functional theory

Journal of computational chemistry, 2020-08, Vol.41 (21), p.1931-1940

2020

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LOBSTER: A tool to extract chemical bonding from plane-wave based DFT

Journal of computational chemistry, 2016-04, Vol.37 (11), p.1030-1035

2016

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Data-Driven Learning of Total and Local Energies in Elemental Boron

Physical review letters, 2018-04, Vol.120 (15), p.156001-156001, Article 156001

2018

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Crystal Orbital Hamilton Population (COHP) Analysis As Projected from Plane-Wave Basis Sets

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2011-06, Vol.115 (21), p.5461-5466

2011

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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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Analytic projection from plane-wave and PAW wavefunctions and application to chemical-bonding analysis in solids

Journal of computational chemistry, 2013-11, Vol.34 (29), p.2557-2567

2013

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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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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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Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

The Journal of chemical physics, 2018-06, Vol.148 (24), p.241714-241714

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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Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra

Chemistry of materials, 2019-11, Vol.31 (22), p.9243-9255

2019

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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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Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

Chemistry of materials, 2019-11, Vol.31 (22), p.9256-9267

2019

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Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon

Angewandte Chemie (International ed.), 2019-05, Vol.58 (21), p.7057-7061

International ed. in English, 2019

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Extracting Crystal Chemistry from Amorphous Carbon Structures

Chemphyschem, 2017-04, Vol.18 (8), p.873-877

2017

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Bonding Nature of Local Structural Motifs in Amorphous GeTe

Angewandte Chemie (International ed.), 2014-09, Vol.53 (40), p.10817-10820

International ed. in English, 2014

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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

2024

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Towards an atomistic understanding of disordered carbon electrode materials

Chemical communications (Cambridge, England), 2018, Vol.54 (47), p.5988-5991

2018

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Data-driven learning and prediction of inorganic crystal structures

Faraday discussions, 2018-10, Vol.211, p.45-59

2018

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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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Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

Molecular simulation, 2018-07, Vol.44 (11), p.866-880

2018

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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Machine learning based interatomic potential for amorphous carbon

LOBSTER: Local orbital projections, atomic charges, and chemical‐bonding analysis from projector‐augmented‐wave‐based density‐functional theory

LOBSTER: A tool to extract chemical bonding from plane-wave based DFT

Data-Driven Learning of Total and Local Energies in Elemental Boron

Crystal Orbital Hamilton Population (COHP) Analysis As Projected from Plane-Wave Basis Sets

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

Analytic projection from plane-wave and PAW wavefunctions and application to chemical-bonding analysis in solids

A Quantum‐Mechanical Map for Bonding and Properties in Solids

Origins of structural and electronic transitions in disordered silicon

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

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

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

Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra

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

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

Understanding X‑ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon

Extracting Crystal Chemistry from Amorphous Carbon Structures

Bonding Nature of Local Structural Motifs in Amorphous GeTe

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

Towards an atomistic understanding of disordered carbon electrode materials

Data-driven learning and prediction of inorganic crystal structures

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

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

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