自动化
材料科学
计算机科学
趋同(经济学)
人工生命
人工智能
系统工程
工程类
机械工程
经济
经济增长
作者
Yunchao Xie,Kianoosh Sattari,Chi Zhang,Jian Lin
标识
DOI:10.1016/j.pmatsci.2022.101043
摘要
The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed challenges and opportunities in an endeavor to develop the next-generation AEPs for ultimately realizing an autonomous or self-driving laboratory. This review will provide insights for researchers aiming to learn the frontier of ML in materials and deploy AEPs in their labs for accelerating material development.
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