Next-generation intelligent laboratories for materials design and manufacturing

被引：14
|
作者：
Peng, Xiting [1 ]
Wang, Xiaonan [1 ,2 ]
Brown, Keith A. [3 ]
Abolhasani, Milad [4 ]
机构：
[1] Tsinghua Univ, Dept Chem Engn, Beijing, Peoples R China
[2] Tsinghua Univ, Key Lab Ind Biocatalysis, Minist Educ, Beijing, Peoples R China
[3] Boston Univ, Mech Engn Mat Sci & Engn & Phys, Boston, MA USA
[4] North Carolina State Univ, Dept Chem & Biomol Engn, Raleigh, NC USA
来源：
MRS BULLETIN | 2023年 / 48卷 / 02期
关键词：
Machine learning; Artificial intelligence; Intelligent laboratory; Autonomous; Materials discovery; GAUSSIAN-PROCESSES; OPTIMIZATION; EXPERIMENTATION; AUTOMATION; DISCOVERY; CAPTURE; SYSTEM;
D O I：
10.1557/s43577-023-00481-z
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.
引用
收藏
页码：179 / 185
页数：7
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