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.
Original language | English (US) |
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Article number | 16028 |
Journal | npj Computational Materials |
Volume | 2 |
DOIs | |
State | Published - Aug 26 2016 |
Funding
This work was performed under the following financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). In addition, AA and AC were supported in part by the following grants: DARPA SIMPLEX award N66001-15-C-4036; NSF awards IIS-1343639 and CCF-1409601; DOE award DESC0007456; and AFOSR award FA9550-12-1-0458. LW was partially supported by the Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Program
ASJC Scopus subject areas
- Modeling and Simulation
- General Materials Science
- Mechanics of Materials
- Computer Science Applications