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

Article · Bryce Meredig, Erin Antono, Carena Church, Maxwell Hutchinson, Julia Ling, Sean P. Paradiso, Ben J. Blaiszik, Ian T Foster, Brenna M. Gibbons, Jason R. Hattrick-Simpers, Apurva Mehta, Logan T. Ward

- Archive id: doi:10.1039/c8me00012c
- Published: 2018-01-01
- Venue: Molecular Systems Design & Engineering
- DOI: https://doi.org/10.1039/c8me00012c
- Citations: 268
- Source URL: https://doi.org/10.1039/c8me00012c
- PDF: https://pubs.rsc.org/en/content/articlepdf/2018/me/c8me00012c
- Provenance: openalex

## Abstract

Traditional machine learning (ML) metrics overestimate model performance for materials discovery.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [AI for Science](https://space-kitty.com/topics/ai-for-science)

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