Efficient sampling for decision making in materials discovery

• 作者: Yuan Tian Turab Lookman Dezhen Xue
• 来源期刊: 中国物理B（英文版）
• 年，卷（期）: 2021,（5）
• 所属期刊栏目: TOPICAL REVIEW — Machine learning in condensed matter physics
• 页码范围: 1-12
• 页数: 12页
• 语种: 英文
• DOI: 10.1088/1674-1056/abf12d

摘要

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.