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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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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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Origins of structural and electronic transitions in disordered silicon

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

2021

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

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

International ed. in English, 2019

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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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Mapping Materials and Molecules

Accounts of chemical research, 2020-09, Vol.53 (9), p.1981-1991

2020

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Structure and Bonding in Amorphous Red Phosphorus

Angewandte Chemie International Edition, 2023-06, Vol.62 (24), p.e202216658-n/a

International ed. in English, 2023

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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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Struktur und Bindung im amorphen roten Phosphor

Angewandte Chemie, 2023-06, Vol.135 (24), p.n/a

2023

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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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A transferable active-learning strategy for reactive molecular force fields

Chemical science (Cambridge), 2021-08, Vol.12 (32), p.1944-1955

2021

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Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

The journal of physical chemistry. B, 2018-09, Vol.122 (38), p.8998-9006

2018

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Covalency of hydrogen bonds in solids revisited

Chemical communications (Cambridge, England), 2014-10, Vol.50 (78), p.11547-11549

2014

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Exploring the configurational space of amorphous graphene with machine-learned atomic energies

Chemical science (Cambridge), 2022-11, Vol.13 (46), p.1372-13731

2022

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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

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

Origins of structural and electronic transitions in disordered silicon

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

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

Gaussian Process Regression for Materials and Molecules

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

Growth Mechanism and Origin of High sp3 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

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

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

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

Data-driven learning and prediction of inorganic crystal structures

Mapping Materials and Molecules

Structure and Bonding in Amorphous Red Phosphorus

Towards an atomistic understanding of disordered carbon electrode materials

Struktur und Bindung im amorphen roten Phosphor

Bonding Nature of Local Structural Motifs in Amorphous GeTe

A transferable active-learning strategy for reactive molecular force fields

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

Covalency of hydrogen bonds in solids revisited

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

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