Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 142, Issue 3, Pages 1475-1481Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jacs.9b11569
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Funding
- National Science Foundation [1825352, 1933861]
- United States Department of Energy National Energy Technology Laboratory [DE-FE0031645]
- Unite Sates Department of Agriculture [2018-67017-27880]
- University of Missouri
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Herein, we report machine learning algorithms by training data sets from a set of both successful and failed experiments for studying the crystallization propensity of metal-organic nanocapsules (MONCs). Among a variety of studied machine learning algorithms, XGBoost affords the highest prediction accuracy of >90%. The derived chemical feature scores that determine importance of reaction parameters from the XGBoost model assist to identify synthesis parameters for successfully synthesizing new hierarchical structures of MONCs, showing superior performance to a well-trained chemist. This work demonstrates that the machine learning algorithms can assist the chemists to faster search for the optimal reaction parameters from many experimental variables, whose features are usually hidden in the high-dimensional space.
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