The 83rd JSAP Autumn Meeting 2022

Presentation information

Oral presentation

11 Superconductivity » 11.1 Fundamental properties

[22a-A302-1~8] 11.1 Fundamental properties

Thu. Sep 22, 2022 9:15 AM - 11:30 AM A302 (A302)

Masatsune Kato(Tohoku Univ.), Shigeyuki Ishida(AIST)

9:15 AM - 9:30 AM

[22a-A302-1] ML Search for New Superconducting Materials Based on Composition Information and Tc Prediction Map

Kaname Matsumoto1, Tomoya Horide1 (1.Kyutech)

Keywords:superconductor, critical temperature, machine learning

The substance group predicted by ML and the structural database is inevitably a stable substance synthesized in the past, and its structure is limited. However, if the search range is expanded to the non-equilibrium state, there is a good possibility that the optimum combination of substances that matches the Tc prediction of ML can be found. In this research, we will give an overview of the new superconducting material search model by ML so far, and take a step forward to examine a new material search ML method that covers the non-equilibrium state.