S2SNet: A Pretrained Neural Network for Superconductivity Discovery
Ke Liu Kaifan Yang Jiahong Zhang Renjun Xu
Superconductivity allows electrical current to flow without any energy loss,
and thus making solids superconducting is a grand goal of physics, material
science, and electrical engineering. More than 16 Nobel Laureates have been
awarded for their contribution to superconductivity research. Superconductors
are valuable for sustainable development goals (SDGs), such as climate change
mitigation, affordable and clean energy, industry, innovation and
infrastructure, and so on. However, a unified physics theory explaining all
superconductivity mechanism is still unknown. It is believed that
superconductivity is microscopically due to not only molecular compositions but
also the geometric crystal structure. Hence a new dataset, S2S, containing both
crystal structures and superconducting critical temperature, is built upon
SuperCon and Material Project. Based on this new dataset, we propose a novel
model, S2SNet, which utilizes the attention mechanism for superconductivity
prediction. To overcome the shortage of data, S2SNet is pre-trained on the
whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet
makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under
Curve (AUC) of 0.92. To the best of our knowledge, S2SNet is the first work to
predict superconductivity with only information of crystal structures. This
work is beneficial to superconductivity discovery and further SDGs. Code and
datasets are available in https://github.com/zjuKeLiu/S2SNet