Accelerating spin Hall conductivity predictions via machine learning

Abstract

Accurately predicting the spin Hall conductivity (SHC) is crucial for designing novel spintronic devices that leverage the spin Hall effect. First-principles calculations of SHCs are computationally intensive and unsuitable for quick hight-hroughput screening. Here, we have developed a residual crystal graph convolutional neural network (Res-CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. This is enabled by having access to 9249 instances of SHCs data and incorporating extra residual networks into the standard CGCNN framework. We found that Res-CGCNN surpasses CGCNN, achieving a mean absolute error of 115.4 (ħ/e) (S/cm) for regression and an area under the receiver operating characteristic curve of 0.86 for classification. Additionally, we utilized Res-CGCNN to conduct hight-hroughput screenings of materials in the Materials Project database that were absent in the training set. This led to the prediction of several previously unreported materials displaying large SHCs exceeding 1000 (ħ/e) (S/cm), which were validated through first-principles calculations. This study represents the inaugural endeavor to construct a machine learning model capable of effectively capturing the intricate nonlinear relationship between SHCs and crystal structure and composition, serving as a useful tool for the efficient screening and design of materials exhibiting high SHCs.

Keywords

CGCNN / first-principles calculations / machine learning / spin Hall conductivity

References

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