Machine Learning Assisted Synthesis of Metal-Organic Nanocapsules

29 October 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


machine learning
metal-organic nanocapsules
crystallization propensity


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