High-throughput screening of ferroelectric materials for non-volatile random access memory using multilayer perceptrons

During the last several years, the development of combinatorial technology has enabled synthesis of a huge amount of chemical compounds in a short time. The large number of variables makes the direct human interpretation of data derived from combinatorial experimentation for high-throughput screening (HTS) very difficult. Artificial neural networks using multilayer perceptrons (MLP) have been successfully applied to the regression problems with various material data. In this work, MLP model was applied to HTS of ferroelectric materials including Bi_4−xLa_xTi₃O₁₂ (BLT) and Bi_4−xCe_xTi₃O₁₂ (BCT). The model using MLP was made to predict the ferroelectric properties of whole feasible experimental conditions. Once a neural network model with high accuracy and good generalization performance was established, we could predict the expected optimal reaction conditions with the best characteristics. The highest gradient value obtained using MLP model is higher than the maximum value found from experiments, thereby accelerating the discovery of the optimal compositions and post-annealing time of BCT and BLT.