DOI: 10.1039/c8me00012c. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

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

10.1039/c8me00012c

Crossref journal-article

Royal Society of Chemistry (RSC)

Molecular Systems Design & Engineering (292)

Abstract

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

Bibliography

Meredig, B., Antono, E., Church, C., Hutchinson, M., Ling, J., Paradiso, S., Blaiszik, B., Foster, I., Gibbons, B., Hattrick-Simpers, J., Mehta, A., & Ward, L. (2018). Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Molecular Systems Design & Engineering, 3(5), 819â825.

Authors 12

Bryce Meredig (first)
Erin Antono (additional)
Carena Church (additional)
Maxwell Hutchinson (additional)
Julia Ling (additional)
Sean Paradiso (additional)
Ben Blaiszik (additional)
Ian Foster (additional)
Brenna Gibbons (additional)
Jason Hattrick-Simpers (additional)
Apurva Mehta (additional)
Logan Ward (additional)

References 37 Referenced 188

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Dates

Type	When
Created	7 years ago (Aug. 17, 2018, 8:14 a.m.)
Deposited	1 year, 4 months ago (April 17, 2024, 5 p.m.)
Indexed	1 week, 1 day ago (Aug. 12, 2025, 6:18 p.m.)
Issued	7 years, 7 months ago (Jan. 1, 2018)
Published	7 years, 7 months ago (Jan. 1, 2018)
Published Online	7 years, 7 months ago (Jan. 1, 2018)

Funders 2

National Institute of Standards and Technology 10.13039/100000161
Region: Americas
gov (National government)
Labels4
1. U.S. National Institute of Standards and Technology
2. National Institute for Standards and Technology
3. U.S. Department of Commerce's National Institute of Standards and Technology
4. NIST
Awards1
1. 60NANB15D077
U.S. Department of Energy 10.13039/100000015
Region: Americas
gov (National government)
Labels8
1. Energy Department
2. Department of Energy
3. United States Department of Energy
4. ENERGY.GOV
5. US Department of Energy
6. USDOE
7. DOE
8. USADOE
Awards1
1. DE-AC02-06CH11357

BibTeX

@article{Meredig_2018, title={Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery}, volume={3}, ISSN={2058-9689}, url={http://dx.doi.org/10.1039/c8me00012c}, DOI={10.1039/c8me00012c}, number={5}, journal={Molecular Systems Design & Engineering}, publisher={Royal Society of Chemistry (RSC)}, author={Meredig, Bryce and Antono, Erin and Church, Carena and Hutchinson, Maxwell and Ling, Julia and Paradiso, Sean and Blaiszik, Ben and Foster, Ian and Gibbons, Brenna and Hattrick-Simpers, Jason and Mehta, Apurva and Ward, Logan}, year={2018}, pages={819–825} }

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