  Exploration of new superconductors still relies on the experience and
intuition of experts and is largely a process of experimental trial and error.
In one study, only 3% of the candidate materials showed superconductivity.
Here, we report the first deep learning model for finding new superconductors.
We introduced the method named "reading periodic table" which represented the
periodic table in a way that allows deep learning to learn to read the periodic
table and to learn the law of elements for the purpose of discovering novel
superconductors that are outside the training data. It is recognized that it is
difficult for deep learning to predict something outside the training data.
Although we used only the chemical composition of materials as information, we
obtained an $R^{2}$ value of 0.92 for predicting $T_\text{c}$ for materials in
a database of superconductors. We also introduced the method named "garbage-in"
to create synthetic data of non-superconductors that do not exist.
Non-superconductors are not reported, but the data must be required for deep
learning to distinguish between superconductors and non-superconductors. We
obtained three remarkable results. The deep learning can predict
superconductivity for a material with a precision of 62%, which shows the
usefulness of the model; it found the recently discovered superconductor CaBi2
and another one Hf0.5Nb0.2V2Zr0.3, neither of which is in the superconductor
database; and it found Fe-based high-temperature superconductors (discovered in
2008) from the training data before 2008. These results open the way for the
discovery of new high-temperature superconductor families. The candidate
materials list, data, and method are openly available from the link
https://github.com/tomo835g/Deep-Learning-to-find-Superconductors.
