Big Data of Materials Science: Critical Role of the Descriptor
Open accessAbstract
Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.
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Ghiringhelli et al. proposed compressive sensing to discover functional forms and tested stability rules for binary semiconductors.
“Thus, Ghiringhelli et al.203 proposed compressive sensing to discover functional forms and tested stability rules for binary semiconductors.”
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- Authors
- Ghiringhelli, L. M., Vybiral, J., Levchenko, S. V., Draxl, C., & Scheffler, M.
- Affiliations
- Fritz Haber Institute of the Max Planck Society (DE)
- Humboldt-Universität zu Berlin (DE)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2015-03-10
- Volume
- 114
- Issue
- 10
- Pages
- 105503-105503
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2164524421
- Cited by
- 920
- References
- 3
- Citations by year
- 2026: 71 · 2025: 75 · 2024: 77 · 2023: 84 · 2022: 116 · 2021: 97 · 2020: 116 · 2019: 92 · 2018: 94 · 2017: 56 · 2016: 33 · 2015: 9
- FWCI
- 25.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physical Review Letters (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Bulletin of the American Physical Society
- Funders
- Ministry of Education, Youth and Sports of the Czech Republic (LL1203)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- material descriptors, causality, materials property prediction, sphalerite, wurtzite structure, halite, semiconductors, crystal structure, anomaly detection
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