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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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Speeding up quantum dissipative dynamics of open systems with kernel methods

New journal of physics, 2021-11, Vol.23 (11), p.113019

2021

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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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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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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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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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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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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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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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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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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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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: Benchmarks for Ground-State Properties

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

2016

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Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models

The journal of physical chemistry letters, 2023-08, Vol.14 (34), p.7732-7743

2023

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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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Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

The Journal of chemical physics, 2023-02, Vol.158 (7), p.074103-074103

Dral, Pavlo O.

2023

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Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra

The journal of physical chemistry letters, 2024-03, Vol.15 (9), p.2325-2331

2024

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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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Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine

The journal of physical chemistry letters, 2024-04, Vol.15 (16), p.4451-4460

2024

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MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Journal of chemical theory and computation, 2024-02, Vol.20 (3), p.1193-1213

2024

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Semiempirical UNO–CAS and UNO–CI: Method and Applications in Nanoelectronics

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2011-10, Vol.115 (41), p.11303-11312

2011

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Quantum Chemistry in the Age of Machine Learning

Speeding up quantum dissipative dynamics of open systems with kernel methods

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

Nonadiabatic Excited-State Dynamics with Machine Learning

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

Hierarchical machine learning of potential energy surfaces

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

Machine Learning for Absorption Cross Sections

One-Shot Trajectory Learning of Open Quantum Systems Dynamics

Deep Learning for Nonadiabatic Excited-State Dynamics

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

Modern semiempirical electronic structure methods

Themed collection on Insightful Machine Learning for Physical Chemistry

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

Energy-conserving molecular dynamics is not energy conserving

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

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models

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

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra

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

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Semiempirical UNO–CAS and UNO–CI: Method and Applications in Nanoelectronics

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