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Journal Articles

Synergy of semiempirical models and machine learning in computational chemistry

Nikita Fedik, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Maksim Kulichenko, Ying Wai Li, Roman Zubatyuk, Richard Messerly, Olexandr Isayev, Sergei Tretiak

J. Chem. Phys. 159, 110901 (2023)

https://doi.org/10.1063/5.0151833

Published: September 2023

View article titled, Synergy of semiempirical models and machine learning in computational chemistry

Journal Articles

Determining best practices for using genetic algorithms in molecular discovery

Brianna L. Greenstein, Danielle C. Elsey, Geoffrey R. Hutchison

J. Chem. Phys. 159, 091501 (2023)

https://doi.org/10.1063/5.0158053

Published: September 2023

View article titled, Determining best practices for using genetic algorithms in molecular discovery

Includes: Supplementary data

Journal Articles

Mapping the frontier orbital energies of imidazolium-based cations using machine learning

Pratik Dhakal, Wyatt Gassaway, Jindal K. Shah

J. Chem. Phys. 159, 064513 (2023)

https://doi.org/10.1063/5.0155775

Published: August 2023

View article titled, Mapping the frontier orbital energies of imidazolium-based cations using machine learning

Includes: Supplementary data

Journal Articles

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Jicheng Guo, Vanessa Woo, David A. Andersson, Nathaniel Hoyt, Mark Williamson, Ian Foster, Chris Benmore, Nicholas E. Jackson, Ganesh Sivaraman

J. Chem. Phys. 159, 024802 (2023)

https://doi.org/10.1063/5.0153021

Published: July 2023

View article titled, AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Includes: Supplementary data

Journal Articles

Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions

Stiv Llenga, Ganna Gryn’ova

J. Chem. Phys. 158, 214116 (2023)

https://doi.org/10.1063/5.0151122

Published: June 2023

View article titled, Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions

Includes: Supplementary data

Journal Articles

Evaluation of tight-binding DFT performance for the description of organic photochromes properties

Corentin Poidevin, Gwenhaël Duplaix-Rata, Karine Costuas, Arnaud Fihey

J. Chem. Phys. 158, 074303 (2023)

https://doi.org/10.1063/5.0133418

Published: February 2023

View article titled, Evaluation of tight-binding DFT performance for the description of organic photochromes properties

Includes: Supplementary data

Journal Articles

Ligand additivity relationships enable efficient exploration of transition metal chemical space

Naveen Arunachalam, Stefan Gugler, Michael G. Taylor, Chenru Duan, Aditya Nandy, Jon Paul Janet, Ralf Meyer, Jonas Oldenstaedt, Daniel B. K. Chu, Heather J. Kulik

J. Chem. Phys. 157, 184112 (2022)

https://doi.org/10.1063/5.0125700

Published: November 2022

View article titled, Ligand additivity relationships enable efficient exploration of transition metal chemical space

Includes: Supplementary data

Journal Articles

Broad chemical transferability in structure-based coarse-graining

Kiran H. Kanekal, Joseph F. Rudzinski, Tristan Bereau

J. Chem. Phys. 157, 104102 (2022)

https://doi.org/10.1063/5.0104914

Published: September 2022

View article titled, Broad chemical transferability in structure-based coarse-graining

Includes: Supplementary data

Journal Articles

Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen

Emily A. Eikey, Alex M. Maldonado, Charles D. Griego, Guido Falk von Rudorff, John A. Keith

J. Chem. Phys. 156, 204111 (2022)

https://doi.org/10.1063/5.0079487

Published: May 2022

View article titled, Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen

Includes: Supplementary data

Journal Articles

Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells

Brianna L. Greenstein, Danielle C. Hiener, Geoffrey R. Hutchison

J. Chem. Phys. 156, 174107 (2022)

https://doi.org/10.1063/5.0087299

Published: May 2022

View article titled, Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells

Includes: Supplementary data

Journal Articles

Machine learned calibrations to high-throughput molecular excited state calculations

Shomik Verma, Miguel Rivera, David O. Scanlon, Aron Walsh

J. Chem. Phys. 156, 134116 (2022)

https://doi.org/10.1063/5.0084535

Published: April 2022

View article titled, Machine learned calibrations to high-throughput molecular excited state calculations

Includes: Supplementary data

Journal Articles

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik

J. Chem. Phys. 156, 074101 (2022)

https://doi.org/10.1063/5.0082964

Published: February 2022

View article titled, Representations and strategies for transferable machine learning improve model performance in chemical discovery

Includes: Supplementary data

Journal Articles

Evaluating quantum alchemy of atoms with thermodynamic cycles: Beyond ground electronic states

Emily A. Eikey, Alex M. Maldonado, Charles D. Griego, Guido Falk von Rudorff, John A. Keith

J. Chem. Phys. 156, 064106 (2022)

https://doi.org/10.1063/5.0079483

Published: February 2022

View article titled, Evaluating quantum alchemy of atoms with thermodynamic cycles: Beyond ground electronic states

Includes: Supplementary data

Journal Articles

Generating reaction trees with cascaded variational autoencoders

Dai Hai Nguyen, Koji Tsuda

J. Chem. Phys. 156, 044117 (2022)

https://doi.org/10.1063/5.0076749

Published: January 2022

View article titled, Generating reaction trees with cascaded variational autoencoders

Includes: Supplementary data

Journal Articles

NNAIMQ: A neural network model for predicting QTAIM charges

Miguel Gallegos, José Manuel Guevara-Vela, Ángel Martín Pendás

J. Chem. Phys. 156, 014112 (2022)

https://doi.org/10.1063/5.0076896

Published: January 2022

View article titled, NNAIMQ: A neural network model for predicting QTAIM charges

Includes: Supplementary data

Journal Articles

Data-driven modeling of S_{→ S₁ excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design}

Amit Gupta, Sabyasachi Chakraborty, Debashree Ghosh, Raghunathan Ramakrishnan

J. Chem. Phys. 155, 244102 (2021)

https://doi.org/10.1063/5.0076787

Published: December 2021

View article titled, Data-driven modeling of S<sub /> → S<sub>1</sub> excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design

Journal Articles

Arbitrarily accurate quantum alchemy

Guido Falk von Rudorff

J. Chem. Phys. 155, 224103 (2021)

https://doi.org/10.1063/5.0073941

Published: December 2021

View article titled, Arbitrarily accurate quantum alchemy

Journal Articles

Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

Nicolai Ree, Mads Koerstz, Kurt V. Mikkelsen, Jan H. Jensen

J. Chem. Phys. 155, 184105 (2021)

https://doi.org/10.1063/5.0063694

Published: November 2021

View article titled, Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

Includes: Supplementary data

Journal Articles

Entropy-based active learning of graph neural network surrogate models for materials properties

Johannes Allotey, Keith T. Butler, Jeyan Thiyagalingam

J. Chem. Phys. 155, 174116 (2021)

https://doi.org/10.1063/5.0065694

Published: November 2021

View article titled, Entropy-based active learning of graph neural network surrogate models for materials properties

Journal Articles

Exploring human-guided strategies for reaction network exploration: Interactive molecular dynamics in virtual reality as a tool for citizen scientists

Robin J. Shannon, Helen M. Deeks, Eleanor Burfoot, Edward Clark, Alex J. Jones, Adrian J. Mulholland, David R. Glowacki

J. Chem. Phys. 155, 154106 (2021)

https://doi.org/10.1063/5.0062517

Published: October 2021

View article titled, Exploring human-guided strategies for reaction network exploration: Interactive molecular dynamics in virtual reality as a tool for citizen scientists

Includes: Supplementary data

NARROW

Topics

Synergy of semiempirical models and machine learning in computational chemistry

Determining best practices for using genetic algorithms in molecular discovery

Mapping the frontier orbital energies of imidazolium-based cations using machine learning

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions

Evaluation of tight-binding DFT performance for the description of organic photochromes properties

Ligand additivity relationships enable efficient exploration of transition metal chemical space

Broad chemical transferability in structure-based coarse-graining

Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen

Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells

Machine learned calibrations to high-throughput molecular excited state calculations

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Evaluating quantum alchemy of atoms with thermodynamic cycles: Beyond ground electronic states

Generating reaction trees with cascaded variational autoencoders

NNAIMQ: A neural network model for predicting QTAIM charges

Data-driven modeling of S_{→ S₁ excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design}

Arbitrarily accurate quantum alchemy

Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

Entropy-based active learning of graph neural network surrogate models for materials properties

Exploring human-guided strategies for reaction network exploration: Interactive molecular dynamics in virtual reality as a tool for citizen scientists

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