Active learning for optimum experimental design--insight into perovskite oxides

Finding the optimum material with improved properties for a given application is challenging because data acquisition in materials science and chemistry is time consuming and expensive. Therefore, dealing with small datasets is a reality in chemistry, whether the data are obtained from synthesis or computational experiments. In this work, we propose a new artificial intelligence method based on active learning (AL) to guide new experiments with as little data as possible, for optimum experimental design. The AL method is applied to ABO3 perovskites, where a descriptor based on atomic properties was developed. Several regressor algorithms were employed: artificial neural network, Gaussian process, and support vector regressor. The developed AL method was applied in the experimental design of two important materials: non-stoichiometric perovskites (Ba((1-x))AxTi((1-y))B(y)O(3)) due to substituting ionic sites with different concentrations and elements (A = Ca, Sr, Cd; B = Zr, Sn, Hf), aiming at the maximization of the energy storage density, and stoichiometric ABO(3 )perovskites where different elements are changed in the A and B sites for the minimization of the formation energy. AL for experimental design is implemented in the machine learning agent for chemistry and design (MLChem4D) software, which has the potential to be applied in inorganic and organic synthesis (e.g., search for the optimum concentrations, catalysts, reactants, temperatures, and pH to improve the yield) and materials science (e.g., search the periodic table for the proper elements and their concentrations to improve the materials properties). The latter marks the first MLChem4D application for the design of perovskites.