TcPredictor

Room temperature superconductors are probably the most desired systems in solid state physics because of their energy implications: superconductors offer conductivity without energy loss. While many materials can reach this state, only those with a sufficiently high critical temperature (Tc) will have technological applications. The recent discovery of hydrogen-rich materials room temperature superconductors has set up a quest in this direction. However, since theoretical models do not offer today sufficient conditions to find such materials most of the new discoveries have been based on trial-and-error (expensive) experiments. Our recent discovery of the networking value [Nat. Comm 12, 5381 (2021)], should allow us to find new superconducting materials through a cheap estimation of Tc from chemical and electronic structure features, thus avoiding expensive electron-phonon coupling calculations. TcPredictor aims at applying this new index in order to find new binary and trinary superconducting materials with industrial and transportation applications. To make this project possible, we have formed an interdisciplinary consortium (chemistry, physics, computer science): specialists in electronic structure and superconductivity to cover the theoretical aspects, computer engineers to search for new methods of acceleration by machine learning and finally, specialists in phase prediction to assemble the results and propose new room temperature superconductors. The project will be naturally structured in the following WPs: WP1 will aim at improving the performance of the networking value by including bi-electronic and anharmonic effects, WP2) will aim at accelerating the calculation to make it accessible for high-throughput screening, and WP3) will use the newly improved and accelerated index to predict new superconductors.