Bond sensitive graph neural networks for predicting high temperature superconductors
Liang Gu , Yang Liu , Pin Chen , Haiyou Huang , Ning Chen , Yang Li , Turab Lookman , Yutong Lu , Yanjing Su
Materials Genome Engineering Advances ›› 2024, Vol. 2 ›› Issue (2) : 48
Bond sensitive graph neural networks for predicting high temperature superconductors
Finding high temperature superconductors (HTS) has been a continuing challenge due to the difficulty in predicting the transition temperature (Tc) of superconductors. Recently, the efficiency of predicting Tc has been greatly improved via machine learning (ML). Unfortunately, prevailing ML models have not shown adequate generalization ability to find new HTS, yet. In this work, a graph neural network model is trained to predict the maximal Tc (Tcmax) of various materials. Our model reveals a close connection between Tcmax and chemical bonds. It suggests that shorter bond lengths are favored by high Tc, which is in coherence with previous domain knowledge. More importantly, it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high Tc, which is new even to the human experts. It can provide a convenient guidance to the materials scientists in search of HTS.
graph neural network / machine learning / superconductivity / superconductors / transition temperature
2024 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.
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