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

Li, Shaobo; Dan, Yabo; Li, Xiang; Hu, Tianchang; Dong, Rongzhi; Cao, Zhuo; Hu, Jianjun

doi:10.3390/sym12020262

Cited by 25 publications

(16 citation statements)

References 53 publications

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“…The SuperCon database [43] is currently the biggest and most comprehensive database of superconductors in the world. It is free and open to the public, and it has been used in almost all ML studies of superconductors [44][45][46]. The SuperCon dataset was pre-processed for further research by Hamidieh [7], and this database is deposited in the University of California Irvine data repository [47].…”

Section: Datasetmentioning

confidence: 99%

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

Nieto

García–Gonzalo

Paredes–Sánchez

2021

Neural Comput & Applic

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This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

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Section: Datasetmentioning

confidence: 99%

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

Nieto

García–Gonzalo

Paredes–Sánchez

2021

Neural Comput & Applic

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“…In the research (Li et al, 2019), the hybrid neural network that combines a convolution neural network and long short-term memory neural network is proposed to extract the characteristics of materials for critical temperature prediction of superconductors. The superconductor data comes from the Superconducting Material Database maintained by Japan's National Institute for Materials Science.…”

Section: Introductionmentioning

confidence: 99%

“…All these researches (Hamidieh, 2018;Abdulkadir and Kemal, 2019;Li et al, 2019) are using the same dataset.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Prediction of Critical Temperature of Superconductor Materials Described by Chemical Formulas

et al. 2021

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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°.

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“…Machine learning and deep learning [1,2] have been increasingly used in materials discovery recently with a variety of applications [3,4,5] such as rechargeable alkali-Ion batteries, photovoltaics, catalysts, thermoelectrics, superhard materials [6], and superconductors [7]. The two key components of a machine learning model for materials property prediction is the descriptor or feature set and the machine learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction

Louis,

Zhao,

Nasiri

et al. 2020

Preprint

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Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials. More specifically, graph neural network (GNN) has been applied in predicting material properties. In this work, we develop a novel model, GATGNN, for predicting inorganic material properties based on graph neural networks composed of multiple graph-attention layers (GAT) and a global attention layer. Through the application of the GAT layers, our model can efficiently learn the complex bonds shared among the atoms within each atom's local neighborhood. Subsequently, the global attention layer provides the weight coefficients of each atom in the inorganic crystal material which are used to considerably improve our model's performance. Notably, with the development of our GATGNN model, we show that our method is able to both outperform the previous models' predictions and provide insight into the crystallization of the material.Numerous structural descriptors have been proposed to represent materials such as atom-centered symmetry functions, Coulomb matrix, smooth overlap of atomic positions, deep tensor neural networks, many-body tensor representation, and Voronoi tessellation[3]. However, these descriptors all have their own limitations such as not being size-invariant by construction, or basing their representation of infinite crystals on local neighborhoods of atoms in the material [3].

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

Cited by 25 publications

References 53 publications

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

Deep Learning Approach for Prediction of Critical Temperature of Superconductor Materials Described by Chemical Formulas

Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction

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