Issue 5, 2018

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

Abstract

Traditional machine learning (ML) metrics overestimate model performance for materials discovery. We introduce (1) leave-one-cluster-out cross-validation (LOCO CV) and (2) a simple nearest-neighbor benchmark to show that model performance in discovery applications strongly depends on the problem, data sampling, and extrapolation. Our results suggest that ML-guided iterative experimentation may outperform standard high-throughput screening for discovering breakthrough materials like high-Tc superconductors with ML.

Graphical abstract: Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

Article information

Article type
Communication
Submitted
05 mar 2018
Accepted
11 iyl 2018
First published
17 avq 2018
This article is Open Access
Creative Commons BY license

Mol. Syst. Des. Eng., 2018,3, 819-825

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

B. Meredig, E. Antono, C. Church, M. Hutchinson, J. Ling, S. Paradiso, B. Blaiszik, I. Foster, B. Gibbons, J. Hattrick-Simpers, A. Mehta and L. Ward, Mol. Syst. Des. Eng., 2018, 3, 819 DOI: 10.1039/C8ME00012C

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