Autonomous experiments using active learning and AI

被引：35

作者：

Ren, Zhichu ^{[1
]}

Ren, Zekun ^{[2
]}

Zhang, Zhen ^{[1
]}

Buonassisi, Tonio ^{[2
,3
]}

Li, Ju ^{[1
,4
]}

机构：

[1] MIT, Dept Mat Sci & Engn, Cambridge, MA 02139 USA

[2] Xinterra, Singapore, Singapore

[3] MIT, Dept Mech Engn, Cambridge, MA 02139 USA

[4] MIT, Dept Nucl Sci & Engn, Cambridge, MA 02139 USA

来源：

NATURE REVIEWS MATERIALS | 2023年 / 8卷 / 09期

关键词：

D O I：

10.1038/s41578-023-00588-4

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Active learning and automation will not easily liberate humans from laboratory workflows. Before they can really impact materials research, artificial intelligence systems will need to be carefully set up to ensure their robust operation and their ability to deal with both epistemic and stochastic errors. As autonomous experiments become more widely available, it is essential to think about how to embed reproducibility, reconfigurability and interoperability in the design of autonomous labs.

引用

页码：563 / 564

页数：2

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