  Predicting high temperature superconductors has long been a great challenge.
A major difficulty is how to predict the transition temperature Tc of
superconductors. Recently, progress in material informatics has led to a number
of machine learning models predicting Tc, which greatly improves the efficiency
of prediction. Unfortunately, prevailing models have not shown adequate
physical rationality and generalization ability to find new high temperature
superconductors, yet. In this work, in order to give a trustable prediction on
the unexplored materials, we built a bond-sensitive graph neural network
(BSGNN), which is optimized to process the information of chemical bond and
electron interaction in the crystal lattice, to predict the Tc maximum of each
type of superconducting materials. On the basis of the domain knowledge
considered in the data preparation and algorithm design, our model revealed a
relevance between the Tc-Tc maximum and chemical bonds. The results indicate
that shorter bond length is favored by high Tc, which is in accordance with
previous human experience. Moreover, it also shows that some specific chemical
elements are favored by high Tc, which is beyond what human experts already
knew. It gives a convenient guidance for searching high temperature
superconductors in materials database, by ruling out the materials that could
never have high Tc.
