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Toward "On‐Demand" Materials Synthesis and Scientific Discovery through Intelligent Robots.

Authors :
Li, Jiagen
Tu, Yuxiao
Liu, Rulin
Lu, Yihua
Zhu, Xi
Source :
Advanced Science. 4/8/2020, Vol. 7 Issue 7, p1-11. 11p.
Publication Year :
2020

Abstract

A Materials Acceleration Operation System (MAOS) is designed, with unique language and compiler architecture. MAOS integrates with virtual reality (VR), collaborative robots, and a reinforcement learning (RL) scheme for autonomous materials synthesis, properties investigations, and self‐optimized quality assurance. After training through VR, MAOS can work independently for labor and intensively reduces the time cost. Under the RL framework, MAOS also inspires the improved nucleation theory, and feedback for the optimal strategy, which can satisfy the demand on both of the CdSe quantum dots (QDs) emission wavelength and size distribution quality. Moreover, it can work well for extensive coverages of inorganic nanomaterials. MAOS frees the experimental researchers out of the tedious labor as well as the extensive exploration of optimal reaction conditions. This work provides a walking example for the "On‐Demand" materials synthesis system, and demonstrates how artificial intelligence technology can reshape traditional materials science research in the future. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21983844
Volume :
7
Issue :
7
Database :
Academic Search Index
Journal :
Advanced Science
Publication Type :
Academic Journal
Accession number :
142633010
Full Text :
https://doi.org/10.1002/advs.201901957