Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization
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Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates—active learning. However, standard global-scope error metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of 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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