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Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials by Ruihan Wang; Yeshuang Zhong; Xuehua Dong; Meng Du; Haolun Yuan; Yurong Zou; Xin Wang; Zhien Lin; Dingguo Xu is a Chemistry article available to read on EtoBox.

What is Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials about?

Crystalline borates are an important class of functional materials with wide applications in photocatalysis and laser technologies. Obtaining their band gap values in a timely and precise manner is a great challenge in material design due to the issues of computational accuracy and cost of first-principles methods. Although machine learning (ML) techniques have shown great successes in predicting the versatile properties of materials, their practicality is often limited by the data set quality. Here, by using a combination of natural language processing searches and domain knowledge, we built an experimental database of inorganic borates, including their chemical compositions, band gaps, and crystal structures. We performed graph network deep learning to predict the band gaps of borates with accuracy, and the results agreed favorably with experimental measurements from the visible-light to the deep-ultraviolet (DUV) region. For a realistic screening problem, our ML model could correctly identify most of the investigated DUV borates. Furthermore, the extrapolative ability of the model was validated against our newly synthesized borate crystal Ag 3 B 6 O 10 NO 3 , supplemented by the

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Author

Ruihan Wang; Yeshuang Zhong; Xuehua Dong; Meng Du; Haolun Yuan; Yurong Zou; Xin Wang; Zhien Lin; Dingguo Xu

Publisher

American Chemical Society (ACS)

Published

2023

Language

EN

Field

Chemistry (Physical Sciences)