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Chin. Phys. B, 2021, Vol. 30(5): 050705    DOI: 10.1088/1674-1056/abf12d
Special Issue: SPECIAL TOPIC — Machine learning in condensed matter physics
TOPICAL REVIEW—Machine learning in condensed matter physics Prev   Next  

Efficient sampling for decision making in materials discovery

Yuan Tian(田原)1, Turab Lookman2,†, and Dezhen Xue(薛德祯)1,‡
1 State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China;
2 Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Abstract  Accelerating materials discovery crucially relies on strategies that efficiently sample the search space to label a pool of unlabeled data. This is important if the available labeled data sets are relatively small compared to the unlabeled data pool. Active learning with efficient sampling methods provides the means to guide the decision making to minimize the number of experiments or iterations required to find targeted properties. We review here different sampling strategies and show how they are utilized within an active learning loop in materials science.
Keywords:  sampling methods      active learning      decision making      material design      Bayesian optimization  
Received:  08 December 2020      Revised:  17 January 2021      Accepted manuscript online:  24 March 2021
PACS:  07.05.Fb (Design of experiments)  
  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)  
  81.90.+c (Other topics in materials science)  
  07.05.Tp (Computer modeling and simulation)  
Fund: Project supported by the National Key Research and Development Program of China (Grant No. 2017YFB0702401) and the National Natural Science Foundation of China (Grant Nos. 51571156, 51671157, 51621063, and 51931004).
Corresponding Authors:  Turab Lookman, Dezhen Xue     E-mail:;

Cite this article: 

Yuan Tian(田原), Turab Lookman, and Dezhen Xue(薛德祯) Efficient sampling for decision making in materials discovery 2021 Chin. Phys. B 30 050705

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