Deep Learning Model for Finding New Superconductors

Nov. 29 16:50-17:15

*Tomohiko Konno1, Hodaka Kurokawa2, Fuyuki Nabeshima2, Yuki Sakishita2, Ryo Ogawa2, Iwao Hosako1, Atsutaka Maeda2
The National Institute for Information and Communications Technologies1
The University of Tokyo2

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 [1]. Here, we report the first deep learning model for finding new superconductors. We introduced the method named “reading periodic table” that 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 which 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 R2 value of 0.92 for predicting Tc 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.

Keywords: Deep learning, Material search, Materials informatics