A general-purpose machine learning framework for predicting properties of inorganic materials [electronic resource]
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 mak...
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| Online Access: |
Online Access (via OSTI) |
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| Format: | Government Document Electronic eBook |
| Language: | English |
| Published: |
Washington, D.C. : Oak Ridge, Tenn. :
United States. Department of Energy. Office of Science ; distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
2016.
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| Subjects: |
| Summary: | 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. |
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| Item Description: | Published through SciTech Connect. 08/26/2016. ": BFnpjcompumats201628" npj Computational Materials 2 1 ISSN 2057-3960 AM. Logan Ward; Ankit Agrawal; Alok Choudhary; Christopher Wolverton. Northwestern Univ., Evanston, IL (United States) US Department of Commerce. |
| Physical Description: | Article No. 16028 : digital, PDF file. |