Deep Learning Accelerated Gold Nanocluster Synthesis

TL;DR

This paper introduces a deep learning framework that accelerates the synthesis of gold nanoclusters by predicting outcomes and providing insights into complex inorganic reactions with limited data.

Contribution

It presents a novel combination of GCNN and SNN models trained on minimal data to guide inorganic material synthesis and enhance understanding.

Findings

Successful experimental validation of synthesis predictions

Effective prediction with only 54 training examples

Decision tree analysis offers insights into synthesis processes

Abstract

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.