Novel machine learning framework for thermal conductivity prediction by crystal graph convolution embedded ensemble. Issue 3 (29th October 2021)

• Record Type: Journal Article
• Authors: Sun, Yajing; Hu, Wenping
• Is Part Of: SmartMat. Volume 3:Issue 3(2022)
• Page Start: 474
• Page End: 481
• Publication Date: 2021-10-29
• Subjects: crystal graph -- ensemble algorithm -- machine learning framework -- thermal conductivity; Smart materials -- Periodicals; Materials science -- Periodicals; 620.11
• DOI: 10.1002/smm2.1074
• Languages: English
• ISSNs: 2688-819X

Abstract

Abstract: With the advent of the big data era, artificial intelligence technology has penetrated and deeply affected our daily life. In addition, data‐based machine learning algorithms have been applied to physics, chemistry, material science, and other basic science fields. However, the scarcity of data sets is known as the main obstacle to its development. Mining effective information from the limited data samples and building an appropriate machine learning algorithms framework are the major breakthroughs. For solid materials, the intrinsic properties are closely related to their atomic composition and relative positions, namely crystal structures. Here, inspired by the emerging of graph convolution neural network and material crystal graph, we proposed an integrated algorithms framework embedded crystal graph to train and predict the lattice thermal conductivities of crystal materials. This machine learning algorithms framework showed superior learning and generalization ability. In addition, not only in predicting thermal conductivities, but our framework also has great performance in predicting other phonon or electron‐related properties. This strategy provided a new approach in the design of machine learning framework, which indicated the great potential for the application of machine learning in material science. Abstract : A crystal graph convolution embedded ensemble machine learning framework is proposed and giving excellent performs in thermal conductivity prediction. In addition, it also does well in the prediction of other phonon and electronic properties, for embedding the crystal graph hidden information. It provides a novel idea for the wide application of machine learning in material science.