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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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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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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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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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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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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 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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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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Modelling and understanding battery materials with machine-learning-driven atomistic simulations

JPhys Energy, 2020-10, Vol.2 (4), p.41003

Deringer, Volker L

2020

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Gaussian Process Regression for Materials and Molecules

Chemical reviews, 2021-08, Vol.121 (16), p.10073-10141

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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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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An accurate and transferable machine learning potential for carbon

The Journal of chemical physics, 2020-07, Vol.153 (3), p.034702

2020

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Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

Physical review letters, 2018-04, Vol.120 (16), p.166101

2018

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Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon

Physical review letters, 2018-04, Vol.120 (16), p.166101-166101

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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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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Cooperativity of Halogen, Chalcogen, and Pnictogen Bonds in Infinite Molecular Chains by Electronic Structure Theory

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2014-05, Vol.118 (17), p.3193-3200

2014

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Nature, Strength, and Cooperativity of the Hydrogen-Bonding Network in α‑Chitin

Biomacromolecules, 2016-03, Vol.17 (3), p.996-1003

2016

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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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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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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Incipient Metals: Functional Materials with a Unique Bonding Mechanism

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

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

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

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

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

A Quantum‐Mechanical Map for Bonding and Properties in Solids

Origins of structural and electronic transitions in disordered silicon

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

Modelling and understanding battery materials with machine-learning-driven atomistic simulations

Gaussian Process Regression for Materials and Molecules

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

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

An accurate and transferable machine learning potential for carbon

Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon

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

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

Cooperativity of Halogen, Chalcogen, and Pnictogen Bonds in Infinite Molecular Chains by Electronic Structure Theory

Nature, Strength, and Cooperativity of the Hydrogen-Bonding Network in α‑Chitin

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

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

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