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.