<p>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 <a>propensity </a>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.</p>