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Theoretical and Computational Chemistry

Working Paper

Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

Natalie Eyke, William H. Green, Klavs F. Jensen

Version 1 posted 18 June 2020

1,074

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Theoretical and Computational Chemistry

Working Paper

Bias Free Multiobjective Active Learning for Materials Design and Discovery

Kevin Maik Jablonka, Giriprasad Melpatti Jothiappan, Shefang Wang, Berend Smit, Brian Yoo

Version 1 posted 09 November 2020

1,102

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Chemical Engineering and Industrial Chemistry

Review

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin, Kevin M. Van Geem

Version 1 posted 12 October 2022

625

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Theoretical and Computational Chemistry

Working Paper

Chemical Space Exploration with Active Learning and Alchemical Free Energies

Yuriy Khalak, Gary Tresadern, David F. Hahn, Bert L. de Groot, Vytautas Gapsys

Version 1 posted 13 July 2022

508

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Theoretical and Computational Chemistry

Working Paper

Active Learning of Many-Body Configuration Space: Application to the Cs+–water MB-nrg Potential Energy Function as a Case Study

Yaoguang Zhai, Alessandro Caruso, Sicun Gao, Paesani Lab

Version 1 posted 23 January 2020

728

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Physical Chemistry

Working Paper

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl

ganesh sivaraman, Jicheng Guo, Logan Ward, Nathaniel Hoyt, Mark Williamson, Ian Foster, Chris Benmore, Nicholas Jackson

Version 1 posted 18 March 2021

724

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Active Learning for Robust, High-Complexity Reactive Atomistic Simulations

Rebecca Lindsey, Laurence E. Fried, Nir Goldman, Sorin Bastea

Version 1 posted 16 July 2020

336

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Catalysis

Working Paper

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Aditya Nandy, Chenru Duan, Conrad Goffinet, Heather Kulik

Version 1 posted 25 February 2022

437

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1

Citations

Theoretical and Computational Chemistry

Working Paper

Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians

Ruben Staub, Stephan Steinmann

Version 1 posted 18 November 2021

354

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Materials Chemistry

Working Paper

Active Meta-Learning for Predicting and Selecting Perovskite Crystallization Experiments

Venkateswaran Shekar, Gareth Nicholas, Mansoor Ani Najeeb, Margaret Zeile, Vincent Yu, Xiaorong Wang, Dylan Slack, Zhi Li, Philip Nega, Emory Chan, Alexander Norquist, Joshua Schrier, Sorelle Friedler

Version 1 posted 27 October 2021

703

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Refine Search

53 results in Active learning in Keywords: Active learning

Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

Bias Free Multiobjective Active Learning for Materials Design and Discovery

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

Chemical Space Exploration with Active Learning and Alchemical Free Energies

Active Learning of Many-Body Configuration Space: Application to the Cs+–water MB-nrg Potential Energy Function as a Case Study

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl

Active Learning for Robust, High-Complexity Reactive Atomistic Simulations

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians

Active Meta-Learning for Predicting and Selecting Perovskite Crystallization Experiments

53 results in Keywords: Active learning