A general-purpose machine learning framework for predicting properties of inorganic materials
Abstract
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.
- Authors:
-
- Northwestern Univ., Evanston, IL (United States). Dept. of Materials Science and Engineering
- Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science
- Publication Date:
- Research Org.:
- Northwestern Univ., Evanston, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); US Dept. of Commerce; USDOD; US Air Force Office of Scientific Research (AFOSR)
- OSTI Identifier:
- 1437349
- Grant/Contract Number:
- SC0007456; N66001-15-C-4036; IIS-1343639; CCF-1409601; FA9550-12-1-0458
- Resource Type:
- Accepted Manuscript
- Journal Name:
- npj Computational Materials
- Additional Journal Information:
- Journal Volume: 2; Journal Issue: 1; Journal ID: ISSN 2057-3960
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Ward, Logan, Agrawal, Ankit, Choudhary, Alok, and Wolverton, Christopher. A general-purpose machine learning framework for predicting properties of inorganic materials. United States: N. p., 2016.
Web. doi:10.1038/npjcompumats.2016.28.
Ward, Logan, Agrawal, Ankit, Choudhary, Alok, & Wolverton, Christopher. A general-purpose machine learning framework for predicting properties of inorganic materials. United States. https://doi.org/10.1038/npjcompumats.2016.28
Ward, Logan, Agrawal, Ankit, Choudhary, Alok, and Wolverton, Christopher. Fri .
"A general-purpose machine learning framework for predicting properties of inorganic materials". United States. https://doi.org/10.1038/npjcompumats.2016.28. https://www.osti.gov/servlets/purl/1437349.
@article{osti_1437349,
title = {A general-purpose machine learning framework for predicting properties of inorganic materials},
author = {Ward, Logan and Agrawal, Ankit and Choudhary, Alok and Wolverton, Christopher},
abstractNote = {A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.},
doi = {10.1038/npjcompumats.2016.28},
journal = {npj Computational Materials},
number = 1,
volume = 2,
place = {United States},
year = {Fri Aug 26 00:00:00 EDT 2016},
month = {Fri Aug 26 00:00:00 EDT 2016}
}
Web of Science
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ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
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Anharmonic thermodynamics of vacancies using a neural network potential
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Data-centric science for materials innovation
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ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
journal, January 2020
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Machine Learning for Parameter Auto-tuning in Molecular Dynamics Simulations: Efficient Dynamics of Ions near Polarizable Nanoparticles
text, January 2019
- Kadupitiya, Jcs; Fox, Geoffrey C.; Jadhao, Vikram
- Unpublished
Representation of compounds for machine-learning prediction of physical properties
text, January 2016
- Seko, Atsuto; Hayashi, Hiroyuki; Nakayama, Keita
- arXiv
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
text, January 2018
- Choudhary, Kamal; DeCost, Brian; Tavazza, Francesca
- arXiv
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
text, January 2019
- Seko, Atsuto; Togo, Atsushi; Tanaka, Isao
- arXiv
A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
text, January 2019
- Wang, Qi; Jain, Anubhav
- arXiv
Predicting the Curie temperature of ferromagnets using machine learning
text, January 2019
- Nelson, James; Sanvito, Stefano
- arXiv
Exploring effective charge in electromigration using machine learning
text, January 2019
- Liu, Yu-chen; Afflerbach, Benjamin; Jacobs, Ryan
- arXiv
Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions
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Identifying an efficient, thermally robust inorganic phosphor host via machine learning
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ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
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- Scientific Reports, Vol. 8, Issue 1