Training artificial neural network for optimization of nanostructured VO₂-based smart window performance

In this work, we apply for the first time a machine learning approach to design and optimize VO₂ based nanostructured smart window performance. An artificial neural network was trained to find the relationship between VO₂ smart window structural parameters and performance metrics-luminous transmittance (T_lum) and solar modulation (ΔT_sol), calculated by first-principle electromagnetic simulations (FDTD method). Once training was accomplished, the combination of optimal T_lum and ΔT_sol was found by applying classical trust region algorithm on the trained network. The proposed method allows flexibility in definition of the optimization problem and provides clear uncertainty limits for future experimental realizations.