A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
Open accessAbstract
Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k -nary compounds of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Hölder means to construct descriptors that generalize over chemistry and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli ( K and G , respectively) for polycrystalline inorganic compounds, using 1,940 compounds from a growing database of calculated elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.
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De Jong et al. used statistical learning to study elastic moduli of inorganic crystals.
“De Jong et al.211 used statistical learning to study elastic moduli of inorganic crystals, and with many other relevant studies.”
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- Authors
- de Jong, M., Chen, W., Notestine, R., Persson, K., Ceder, G., Jain, A., Asta, M., & Gamst, A.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- San Diego Supercomputer Center (US)
- Venue
- Scientific Reports
- Published
- 2016-10-03
- Volume
- 6
- Issue
- 1
- Pages
- 34256-34256
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5046120
- OpenAlex
- W2527749992
- Cited by
- 243
- References
- 4
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 23 · 2023: 21 · 2022: 37 · 2021: 32 · 2020: 33 · 2019: 29 · 2018: 26 · 2017: 16 · 2016: 1
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (05CH11231, No. DE-AC02-05-CH11231, DE AC02 05CH11231)
- Office of Science (DEAC02–05CH11231, 05CH11231)
- Basic Energy Sciences (05CH11231, DE-AC02-05CH11231.)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Earth and Planetary Sciences › Geophysics › High-pressure geophysics and materials
- Keywords
- statistical learning, elastic modulus, bulk modulus, shear modulus, power means, gradient boosting, superhard materials, materials discovery, crystal structure descriptors
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