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Human versus Robots in the Discovery and Crystallization of Gigantic Polyoxometalates
Author(s) -
Duros Vasilios,
Grizou Jonathan,
Xuan Weimin,
Hosni Zied,
Long DeLiang,
Miras Haralampos N.,
Cronin Leroy
Publication year - 2017
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.201705721
Subject(s) - crystallization , crystal (programming language) , crystallography , robot , cluster (spacecraft) , yield (engineering) , computer science , ring (chemistry) , molecule , polyoxometalate , materials science , chemistry , artificial intelligence , algorithm , organic chemistry , metallurgy , programming language , catalysis
The discovery of new gigantic molecules formed by self‐assembly and crystal growth is challenging as it combines two contingent events; first is the formation of a new molecule, and second its crystallization. Herein, we construct a workflow that can be followed manually or by a robot to probe the envelope of both events and employ it for a new polyoxometalate cluster, Na 6 [Mo 120 Ce 6 O 366 H 12 (H 2 O) 78 ]⋅200 H 2 O ( 1 ) which has a trigonal‐ring type architecture (yield 4.3 % based on Mo). Its synthesis and crystallization was probed using an active machine‐learning algorithm developed by us to explore the crystallization space, the algorithm results were compared with those obtained by human experimenters. The algorithm‐based search is able to cover ca. 9 times more crystallization space than a random search and ca. 6 times more than humans and increases the crystallization prediction accuracy to 82.4±0.7 % over 77.1±0.9 % from human experimenters.