Title: Deep Learning Accelerated Gold Nanocluster Synthesis Show Chinese title

Title: 深度学习加速金纳米团簇合成

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. Show Chinese abstract

Abstract: 对非有机反应的理解，尤其是远离平衡状态的反应，由于其固有的复杂性而相对有限。 对基础合成化学的不充分理解限制了高效合成路线的设计，以获得所需的最终产物，特别是那些在原子精度下的无机材料。 在本研究中，使用原子精确金纳米团簇的合成作为演示平台，我们成功开发了一个深度学习框架，用于指导材料合成并加速整个工作流程。 仅使用54个例子，基于基本描述符的图卷积神经网络（GCNN）和孪生神经网络（SNN）分类模型已被训练。 通过成功的实验验证，证明了预测目标合成结果的能力。 此外，可以从由训练良好的分类模型生成的大量数据训练的决策树中获得对合成过程的理解。 这项研究不仅提供了一种加速金纳米团簇合成的数据驱动方法，还为在数据量较少的情况下理解复杂的无机材料合成提供了启示。