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

Engineering and Technology

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

Y. Kim, E. Kim, et al.

This paper by Yoolhee Kim, Edward Kim, Erin Antono, Bryce Meredig, and Julia Ling presents groundbreaking metrics designed to enhance materials discovery. Discover how the predicted fraction of improved candidates (PFIC) and cumulative maximum likelihood of improvement (CMLI) can fast-track your understanding of design spaces in materials discovery, providing high precision for optimal outcomes.... show more
Citation Metrics
Citations
43
Influential Citations
3
Reference Count
29
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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