  The understanding of inorganic reactions, especially those far from the
equilibrium state, is relatively limited due to their inherent complexity. Poor
understandings on the underlying synthetic chemistry have constrained the
design of efficient synthesis routes towards desired final products, especially
those inorganic materials at atomic precision. In this work, using the
synthesis of atomically precise gold nanoclusters as a demonstration platform,
we have successfully developed a deep learning framework for guiding material
synthesis and accelerating the whole workflow. With only 54 examples, the
proposed Graph Convolutional Neural Networks (GCNN) plus Siamese Neural
Networks (SNN) classification model with the basic descriptors have been
trained. The capability of predicting the target synthesis results has been
demonstrated with a successful experimental validation. In addition,
understandings in the synthesis process can be acquired from a decision tree
trained by a large amount of generated data from the well-trained
classification model. This study not only provides a data-driven method
accelerating gold nanocluster synthesis, but also sheds light on understanding
complex inorganic materials synthesis with low data amount.
