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arXiv · 1911.03224

Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization

Zachary del Rosario·Matthias Rupp·Yoolhee Kim·Erin Antono·Julia Ling
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Abstract

Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of \emph{Pareto shell-scope error} to help judge the suitability of a model for proposing material candidates. Further, through synthetic cases and a thermoelectric dataset, we probe the relation between acquisition function fidelity and active learning performance. Results suggest novel diagnostic tools, as well as new insights for acquisition function design.

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Zachary del Rosario, Matthias Rupp, Yoolhee Kim, Erin Antono, Julia Ling. 2019-11-06. Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization. https://arxiv.org/abs/1911.03224

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