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

Journal Article · · Molecular Systems Design & Engineering
DOI:https://doi.org/10.1039/C8ME00012C· OSTI ID:1464980
ORCiD logo [1];  [1];  [1];  [1];  [1];  [1]; ORCiD logo [2]; ORCiD logo [2];  [3]; ORCiD logo [4];  [5]; ORCiD logo [2]
  1. Citrine Informatics, USA
  2. University of Chicago, USA, Argonne National Laboratory, USA
  3. Stanford University, USA
  4. National Institute of Standards and Technology, USA
  5. SLAC National Accelerator Laboratory, USA
+ Show Author Affiliations

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.

Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE; National Institute of Standards and Technology (NIST)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1464980
Alternate ID(s):
OSTI ID: 1484501
Journal Information:
Molecular Systems Design & Engineering, Journal Name: Molecular Systems Design & Engineering Vol. 3 Journal Issue: 5; ISSN 2058-9689
Publisher:
Royal Society of Chemistry (RSC)Copyright Statement
Country of Publication:
United Kingdom
Language:
English
Citation Metrics:
Cited by: 90 works
Citation information provided by
Web of Science

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Cited By (6)

Recent advances and applications of machine learning in solid-state materials science journal August 2019
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics journal July 2019
Data‐Driven Materials Science: Status, Challenges, and Perspectives journal September 2019
Concurrent Optimization of Organic Donor–Acceptor Pairs through Machine Learning journal September 2019
A Critical Review of Machine Learning of Energy Materials journal January 2020
Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning journal January 2019

Figures / Tables (5)

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Related Subjects

75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY