Materials Acceleration Platforms: On the way to autonomous experimentation

被引:82
|
作者
Flores-Leonar, Martha M. [1 ]
Mejia-Mendoza, Luis M. [1 ]
Aguilar-Granda, Andres [1 ]
Sanchez-Lengeling, Benjamin [2 ]
Tribukait, Hermann [3 ]
Amador-Bedolla, Carlos [4 ]
Aspuru-Guzik, Alan [1 ,5 ,6 ,7 ]
机构
[1] Univ Toronto, Dept Chem, Toronto, ON M5S 3H6, Canada
[2] Google Res, Brain Team, Cambridge, MA 02142 USA
[3] ChemOS Inc, Menlo Pk, CA 94025 USA
[4] Univ Nacl Autonoma Mexico, Fac Quim, Mexico City 04510, DF, Mexico
[5] Univ Toronto, Dept Comp Sci, Toronto, ON M5S 3H6, Canada
[6] Vector Inst Artificial Intelligence, Toronto, ON M5S 1M1, Canada
[7] Canadian Inst Adv Res CIFAR, Toronto, ON M5S 1M1, Canada
关键词
OPTIMIZATION; AUTOMATION; REACTIONWARE; TECHNOLOGY; MOLECULES;
D O I
10.1016/j.cogsc.2020.100370
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Materials Acceleration Platforms are an emerging paradigm to accelerate materials discovery as an effort to develop technology solutions that can help address or mitigate climate change concerns. These platforms combine artificial intelligence, robotic systems, and high-performance computing to achieve autonomous experimentation. Nevertheless, their development faces challenges to achieve full autonomy. In this work, we present state-of-the-art robotic platforms and machine learning approaches for autonomous experimentation, their integration, and applications, particularly in the field of materials for clean energy technologies. Later, we discuss the challenges and suggest improvements to be considered in the endeavor to accomplish autonomous experimentation.
引用
收藏
页数:9
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