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Materials discovery is often compared to the challenge of finding a needle ina haystack. While much work has focused on accurately predicting the propertiesof candidate materials with machine learning (ML), which amounts to evaluatingwhether a given candidate is a piece of straw or a needle, less attention hasbeen paid to a critical question: Are we searching in the right haystack? Werefer to the haystack as the design space for a particular materials discoveryproblem (i.e. the set of possible candidate materials to synthesize), and thusframe this question as one of design space selection. In this paper, weintroduce two metrics, the Predicted Fraction of Improved Candidates (PFIC),and the Cumulative Maximum Likelihood of Improvement (CMLI), which wedemonstrate can identify discovery-rich and discovery-poor design spaces,respectively. Using CMLI and PFIC together to identify optimal design spacescan significantly accelerate ML-driven materials discovery.

Machine-learned metrics for predicting the likelihood of success in materials discovery