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Issue 5, 2018
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Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

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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

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Article information


Submitted
05 Mar 2018
Accepted
11 Jul 2018
First published
17 Aug 2018

This article is Open Access

Mol. Syst. Des. Eng., 2018,3, 819-825
Article type
Communication

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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    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
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    [Original citation] - Published by The Royal Society of Chemistry.

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