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Machine Learning Assisted Synthesis of Metal-Organic Nanocapsules

preprint
submitted on 25.10.2019 and posted on 29.10.2019 by Yunchao xie, Chen Zhang, Xiangquan Hu, Chi Zhang, Steven P. Kelley, Jerry L. Atwood, Jian Lin

Herein, we report the successful discovery of a new hierarchical structure of metal-organic nanocapsules (MONCs) by integrating chemical intuition and machine learning algorithms. By training datasets from a set of both succeeded and failed experiments, we studied the crystallization propensity of metal-organic nanocapsules (MONCs). Among four machine learning models, XGB model affords the highest prediction accuracy of 91%. The derived chemical feature scores and chemical hypothesis from the XGB model assist to identify proper synthesis parameters showing superior performance to a well-trained chemist. This paper will shed light on the discovery of new crystalline inorganic-organic hybrid materials guided by machine learning algorithms.

History

Email Address of Submitting Author

linjian@missouri.edu

Institution

University of Missouri

Country

United States

ORCID For Submitting Author

0000-0002-4675-2529

Declaration of Conflict of Interest

No conflict of interest

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in Journal of the American Chemical Society

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