Document Type
Article
Abstract
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.
Digital Object Identifier (DOI)
Publication Info
Published in Symmetry, Volume 12, Issue 2, 2020, pages 262-.
APA Citation
Li, S., Dan, Y., Li, X., Hu, T., Dong, R., Cao, Z., & Hu, J. (2020). Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning. Symmetry, 12(2), 262. https://doi.org/10.3390/sym12020262
Rights
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)