Publikation: Assessing the frontier : Active learning, model accuracy, and multi-objective candidate discovery and optimization
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The Journal of Chemical Physics. American Institute of Physics (AIP). 2020, 153(2), 024112. ISSN 0021-9606. eISSN 1089-7690. Available under: doi: 10.1063/5.0006124
Zusammenfassung
Discovering novel chemicals and materials can be greatly accelerated by iterative machine learning-informed proposal of candidates-active learning. However, standard global error metrics for model quality are not predictive of discovery performance and can be misleading. We introduce the notion of Pareto shell error to help judge the suitability of a model for proposing candidates. Furthermore, through synthetic cases, an experimental thermoelectric dataset and a computational organic molecule dataset, we probe the relation between acquisition function fidelity and active learning performance. Results suggest novel diagnostic tools, as well as new insights for the acquisition function design.
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DEL ROSARIO, Zachary, Matthias RUPP, Yoolhee KIM, Erin ANTONO, Julia LING, 2020. Assessing the frontier : Active learning, model accuracy, and multi-objective candidate discovery and optimization. In: The Journal of Chemical Physics. American Institute of Physics (AIP). 2020, 153(2), 024112. ISSN 0021-9606. eISSN 1089-7690. Available under: doi: 10.1063/5.0006124BibTex
@article{DelRosario2020-07-14Asses-52563,
year={2020},
doi={10.1063/5.0006124},
title={Assessing the frontier : Active learning, model accuracy, and multi-objective candidate discovery and optimization},
number={2},
volume={153},
issn={0021-9606},
journal={The Journal of Chemical Physics},
author={Del Rosario, Zachary and Rupp, Matthias and Kim, Yoolhee and Antono, Erin and Ling, Julia},
note={Article Number: 024112}
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<dcterms:abstract xml:lang="eng">Discovering novel chemicals and materials can be greatly accelerated by iterative machine learning-informed proposal of candidates-active learning. However, standard global error metrics for model quality are not predictive of discovery performance and can be misleading. We introduce the notion of Pareto shell error to help judge the suitability of a model for proposing candidates. Furthermore, through synthetic cases, an experimental thermoelectric dataset and a computational organic molecule dataset, we probe the relation between acquisition function fidelity and active learning performance. Results suggest novel diagnostic tools, as well as new insights for the acquisition function design.</dcterms:abstract>
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