期刊
CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE
卷 21, 期 3, 页码 121-128出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cossms.2016.10.002
关键词
Experimental design; Adaptive learning; Statistical inference; Materials design
资金
- Los Alamos National Laboratory (LANL) Laboratory Directed Research and Development (LDRD) DR project on Materials Informatics [20140013DR]
A key aspect of the developing field of materials informatics is optimally guiding experiments or calculations towards parts of the relatively vast feature space where a material with desired property may be discovered. We discuss our approach to adaptive experimental design and the methods developed in decision theory and global optimization which can be used in materials science. We show that the use of uncertainties to trade-off exploration versus exploitation to guide new experiments or calculations generally leads to enhanced performance, highlighting the need to evaluate and incorporate errors in predictive materials design. We illustrate our ideas on a computed data set of M(2)AX phases generated using ab initio calculations to find the sample with the optimal elastic properties, and discuss how our approach leads to the discovery of new NiTi-based alloys with the smallest thermal dissipation. (C) 2016 Elsevier Ltd. All rights reserved.
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