U.S. Department of EnergyOffice of Scientific and Technical Information
A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
Abstract
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.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Dept. of Materials Science and Engineering
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Technologies Area
- Univ. of California, San Diego, CA (United States). Computational and Applied Statistics Laboratory
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials Sciences Division
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1377526
- Grant/Contract Number:
- AC02-05CH11231; EDCBEE
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
de Jong, Maarten, Chen, Wei, Notestine, Randy, Persson, Kristin, Ceder, Gerbrand, Jain, Anubhav, Asta, Mark, and Gamst, Anthony. A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds. United States: N. p., 2016.
Web. doi:10.1038/srep34256.
de Jong, Maarten, Chen, Wei, Notestine, Randy, Persson, Kristin, Ceder, Gerbrand, Jain, Anubhav, Asta, Mark, & Gamst, Anthony. A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds. United States. https://doi.org/10.1038/srep34256
de Jong, Maarten, Chen, Wei, Notestine, Randy, Persson, Kristin, Ceder, Gerbrand, Jain, Anubhav, Asta, Mark, and Gamst, Anthony. 2016.
"A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds". United States. https://doi.org/10.1038/srep34256. https://www.osti.gov/servlets/purl/1377526.
@article{osti_1377526,
title = {A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds},
author = {de Jong, Maarten and Chen, Wei and Notestine, Randy and Persson, Kristin and Ceder, Gerbrand and Jain, Anubhav and Asta, Mark and Gamst, Anthony},
abstractNote = {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.},
doi = {10.1038/srep34256},
url = {https://www.osti.gov/biblio/1377526},
journal = {Scientific Reports},
issn = {2045-2322},
number = 1,
volume = 6,
place = {United States},
year = {Mon Oct 03 00:00:00 EDT 2016},
month = {Mon Oct 03 00:00:00 EDT 2016}
}
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