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Quantum Chemistry in the Age of Machine Learning

The journal of physical chemistry letters, 2020-03, Vol.11 (6), p.2336-2347

Dral, Pavlo O

2020

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Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

The Journal of chemical physics, 2017-06, Vol.146 (24), p.244108-244108

2017

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Quantum Chemistry in the Age of Machine Learning

Dral, Pavlo O

1, 2022

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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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Nonadiabatic Excited-State Dynamics with Machine Learning

The journal of physical chemistry letters, 2018-10, Vol.9 (19), p.5660-5663

2018

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AI in computational chemistry through the lens of a decade-long journey

Chemical communications (Cambridge, England), 2024-03, Vol.6 (24), p.324-3258

Dral, Pavlo O

2024

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Doped Polycyclic Aromatic Hydrocarbons as Building Blocks for Nanoelectronics: A Theoretical Study

Journal of organic chemistry, 2013-03, Vol.78 (5), p.1894-1902

2013

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The Relationship between Threshold Voltage and Dipolar Character of Self-Assembled Monolayers in Organic Thin-Film Transistors

Journal of the American Chemical Society, 2012-08, Vol.134 (30), p.12648-12652

2012

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Big Data Meets Quantum Chemistry Approximations: The Δ‑Machine Learning Approach

Journal of chemical theory and computation, 2015-05, Vol.11 (5), p.2087-2096

2015

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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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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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Machine Learning for Absorption Cross Sections

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2020-09, Vol.124 (35), p.7199-7210

2020

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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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The Impact of Aggregation on the Photophysics of Spiro‐Bridged Heterotriangulenes

Angewandte Chemie International Edition, 2020-09, Vol.59 (37), p.16233-16240

International ed. in English, 2020

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One-Shot Trajectory Learning of Open Quantum Systems Dynamics

The journal of physical chemistry letters, 2022-07, Vol.13 (26), p.6037-6041

2022

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Interpretable Machine Learning of Two‐Photon Absorption

Advanced science, 2023-03, Vol.10 (8), p.e2204902-n/a

2023

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Configurationally Stable Chiral Dithia‐Bridged Hetero[4]helicene Radical Cation: Electronic Structure and Absolute Configuration

Chemistry, an Asian journal, 2017-01, Vol.12 (1), p.31-35

2017

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Deep Learning for Nonadiabatic Excited-State Dynamics

The journal of physical chemistry letters, 2018-12, Vol.9 (23), p.6702-6708

2018

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Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

Journal of chemical theory and computation, 2015-05, Vol.11 (5), p.2120-2125

2015

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Modern semiempirical electronic structure methods

The Journal of chemical physics, 2024-01, Vol.160 (4)

2024

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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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Themed collection on Insightful Machine Learning for Physical Chemistry

Physical chemistry chemical physics : PCCP, 2023-08, Vol.25 (34), p.22563-22564

2023

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Energy-conserving molecular dynamics is not energy conserving

Physical chemistry chemical physics : PCCP, 2023-09, Vol.25 (35), p.23467-23476

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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Quantum Chemistry in the Age of Machine Learning

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

Quantum Chemistry in the Age of Machine Learning

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

Nonadiabatic Excited-State Dynamics with Machine Learning

AI in computational chemistry through the lens of a decade-long journey

Doped Polycyclic Aromatic Hydrocarbons as Building Blocks for Nanoelectronics: A Theoretical Study

The Relationship between Threshold Voltage and Dipolar Character of Self-Assembled Monolayers in Organic Thin-Film Transistors

Big Data Meets Quantum Chemistry Approximations: The Δ‑Machine Learning Approach

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

Hierarchical machine learning of potential energy surfaces

Machine Learning for Absorption Cross Sections

Choosing the right molecular machine learning potential

The Impact of Aggregation on the Photophysics of Spiro‐Bridged Heterotriangulenes

One-Shot Trajectory Learning of Open Quantum Systems Dynamics

Interpretable Machine Learning of Two‐Photon Absorption

Configurationally Stable Chiral Dithia‐Bridged Hetero[4]helicene Radical Cation: Electronic Structure and Absolute Configuration

Deep Learning for Nonadiabatic Excited-State Dynamics

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

Modern semiempirical electronic structure methods

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

Themed collection on Insightful Machine Learning for Physical Chemistry

Energy-conserving molecular dynamics is not energy conserving

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

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

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