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Quantum Chemistry in the Age of Machine Learning

Dral, Pavlo O

1, 2022

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Artificial intelligence-enhanced quantum chemical method with broad applicability

Nature communications, 2021-12, Vol.12 (1), p.7022-7022, Article 7022

2021

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MLatom: A program package for quantum chemical research assisted by machine learning

Journal of computational chemistry, 2019-10, Vol.40 (26), p.2339-2347

Dral, Pavlo O.

2019

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Hierarchical machine learning of potential energy surfaces

The Journal of chemical physics, 2020-05, Vol.152 (20), p.204110-204110

2020

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Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

Journal of chemical theory and computation, 2019-03, Vol.15 (3), p.1743-1760

2019

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Choosing the right molecular machine learning potential

Chemical science (Cambridge), 2021-11, Vol.12 (43), p.14396-14413

2021

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VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces

Scientific data, 2022-03, Vol.9 (1), p.84-84, Article 84

2022

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Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces

Journal of chemical theory and computation, 2023-04, Vol.19 (8), p.2369-2379

2023

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Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters

Journal of chemical theory and computation, 2016-03, Vol.12 (3), p.1082-1096

2016

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Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties

Journal of chemical theory and computation, 2016-03, Vol.12 (3), p.1097-1120

2016

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Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

The Journal of chemical physics, 2023-02, Vol.158 (5), p.054118-054118

2023

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Newton‑X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles

Journal of chemical theory and computation, 2022-11, Vol.18 (11), p.6851-6865

2022

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What is semiempirical molecular orbital theory approximating?

Journal of molecular modeling, 2019-05, Vol.25 (5), p.119-12, Article 119

2019

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MLatom 2: An Integrative Platform for Atomistic Machine Learning

Topics in current chemistry (2016), 2021-08, Vol.379 (4), p.27-27, Article 27

2021

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Stability of Odd‐ Versus Even‐Electron Gas‐Phase (Quasi)Molecular Ions Derived from Pyridine‐Substituted N‐Heterotriangulenes

ChemPlusChem (Weinheim, Germany), 2017-02, Vol.82 (2), p.163-163

2017

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MLatom 2: An Integrative Platform for Atomistic Machine Learning

New Horizons in Computational Chemistry Software, 2022, p.13-53

2022

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Quantum Chemistry in the Age of Machine Learning

Artificial intelligence-enhanced quantum chemical method with broad applicability

MLatom: A program package for quantum chemical research assisted by machine learning

Hierarchical machine learning of potential energy surfaces

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

Choosing the right molecular machine learning potential

VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces

Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

Newton‑X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles

What is semiempirical molecular orbital theory approximating?

MLatom 2: An Integrative Platform for Atomistic Machine Learning

Stability of Odd‐ Versus Even‐Electron Gas‐Phase (Quasi)Molecular Ions Derived from Pyridine‐Substituted N‐Heterotriangulenes

MLatom 2: An Integrative Platform for Atomistic Machine Learning

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