%A Viatkin,Dmitry
%A Garcia-Zapirain,Begonya
%A Méndez-Zorrilla,Amaia
%A Zakharov,Maxim
%D 2021
%J Frontiers in Materials
%C
%F
%G English
%K Convolution Neural Network,superconductor,Neural networks ensemble,critical temperature,LSTM Neural Network
%Q
%R 10.3389/fmats.2021.714752
%W
%L
%M
%P
%7
%8 2021-October-27
%9 Original Research
%#
%! Prediction temperature superconductors materials
%*
%<
%T Deep Learning Approach for Prediction of Critical Temperature of Superconductor Materials Described by Chemical Formulas
%U https://www.frontiersin.org/articles/10.3389/fmats.2021.714752
%V 8
%0 JOURNAL ARTICLE
%@ 2296-8016
%X This paper proposes a novel neural network architecture and its ensembles to predict the critical superconductivity temperature of materials based on their chemical formula. The research describes the methods and processes of extracting data from the chemical formula and preparing these extracted data for use in neural network training using TensorFlow. In our approach, recurrent neural networks are used including long short-term memory layers and neural networks based on one-dimensional convolution layers for data analysis. The proposed model is an ensemble of pre-trained neural network architectures for the prediction of the critical temperature of superconductors based on their chemical formula. The architecture of seven pre-trained neural networks is based on the long short-term memory layers and convolution layers. In the final ensemble, six neural networks are used: one network based on LSTM and four based on convolutional neural networks, and one embedding ensemble of convolution neural networks. LSTM neural network and convolution neural network were trained in 300 epochs. Ensembles of models were trained in 20 epochs. All neural networks are trained in two stages. At both stages, the optimizer Adam was used. In the first stage, training was carried out by the function of losses Mean Absolute Error (MAE) with the value of optimizer learning rate equal to 0.001. In the second stage, the previously trained model was trained by the function of losses Mean Squared Error (MSE) with a learning rate equal to 0.0001. The final ensemble is trained with a learning rate equal to 0.00001. The final ensemble model has the following accuracy values: MAE is 4.068, MSE is 67.272, and the coefficient of determination (R2) is 0.923. The final model can predict the critical temperature for the chemistry formula with an accuracy of 4.068°.