Optimization of high-temperature superconducting multilayer films using artificial intelligence

Abstract

We have studied the possibility of utilizing artificial intelligence (AI) models to optimize high-temperature superconducting (HTS) multilayer structures for applications working in a specific field and temperature range. For this, we propose a new vortex dynamics simulation method that enables unprecedented efficiency in the sampling of training data required by the AI models. The performance of several different types of AI models has been studied, including kernel ridge regression (KRR), gradient-boosted decision tree (GBDT) and neural network. From these, the GBDT based model was observed to be clearly the best fitted for the associated problem. We have demonstrated the use of GBDT for finding optimal multilayer structure at 10 K temperature under 1 T field. The GBDT model predicts that simple doped-undoped bilayer structures, where the vast majority of the film is undoped superconductor, provide the best performance under the given environment. The obtained results coincide well with our previous studies providing further validation for the use of AI in the associated problem. We generally consider the AI models as highly efficient tools for the broad-scale optimization of HTS multilayer structures and suggest them to be used as the foremost method to further push the limits of HTS films for specific applications.

Keywords

• vortex pinning
• high-temperature superconductor
• thin film
• multilayer
• artificial pinning center
• critical current