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Open AccessArticle

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning

1
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
2
College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
3
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
*
Authors to whom correspondence should be addressed.
Symmetry 2020, 12(2), 262; https://doi.org/10.3390/sym12020262
Received: 12 December 2019 / Revised: 24 January 2020 / Accepted: 1 February 2020 / Published: 8 February 2020
(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)
In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical formula of the superconductor. The experimental results of the HNN model trained with 12,413 superconductors were compared with three benchmark neural network algorithms and multiple machine learning algorithms using two commonly used material characterization methods. The experimental results show that the HNN method proposed in this paper can effectively extract the characteristic relationships between the atoms of superconductors, and it has high accuracy in predicting the TC.
Keywords: superconductivity; machine learning; CNN; LSTM; materials informatics superconductivity; machine learning; CNN; LSTM; materials informatics
MDPI and ACS Style

Li, S.; Dan, Y.; Li, X.; Hu, T.; Dong, R.; Cao, Z.; Hu, J. Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning. Symmetry 2020, 12, 262.

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