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Predicting high temperature superconductors has long been a great challenge.A major difficulty is how to predict the transition temperature Tc ofsuperconductors. Recently, progress in material informatics has led to a numberof machine learning models predicting Tc, which greatly improves the efficiencyof prediction. Unfortunately, prevailing models have not shown adequatephysical rationality and generalization ability to find new high temperaturesuperconductors, yet. In this work, in order to give a trustable prediction onthe unexplored materials, we built a bond-sensitive graph neural network(BSGNN), which is optimized to process the information of chemical bond andelectron interaction in the crystal lattice, to predict the Tc maximum of eachtype of superconducting materials. On the basis of the domain knowledgeconsidered in the data preparation and algorithm design, our model revealed arelevance between the Tc-Tc maximum and chemical bonds. The results indicatethat shorter bond length is favored by high Tc, which is in accordance withprevious human experience. Moreover, it also shows that some specific chemicalelements are favored by high Tc, which is beyond what human experts alreadyknew. It gives a convenient guidance for searching high temperaturesuperconductors in materials database, by ruling out the materials that couldnever have high Tc.

Searching High Temperature Superconductors with the assistance of Graph Neural Networks