Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization

Accelerated searches, made possible by machine learning techniques, are of growing interest in materials discovery. A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites (HOIPs). The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large “compositional” space (at times, exceeding 500,000 possible combinations). Selecting a HOIP with desirable characteristics involves choosing different cations, halides, and solvent blends from a diverse palette of options. An unguided search by experimental investigations or molecular simulations is prohibitively expensive. In this work, we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce, and in which the search space is given by binary variables indicating whether a constituent is present or not. We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time (less than 10%) needed to complete an exhaustive search. We find an optimal composition within 15 ± 10 iterations in a HOIP compositional space containing 72 combinations, and within 31 ± 9 iterations when considering mixed halides (240 combinations). Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach. This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.Bayesian optimization: accelerated perovskites and solvent screeningA cheaper and more effective optimization method has been developed to screen composition and solvent interactions in organic–inorganic perovskites. A collaborative team led by Matthias Poloczek from University of Arizona, USA, propose a modified Bayesian optimization method to predict the optimal combination of various hybrid organic–inorganic perovskites and solvents. They screen the intermolecular binding energy of 240 possible perovskite-solvent combinations, finding that the maximum occurs for FAPbI2Cl and tetrahydrothiophene 1-oxide. The use of an application-specific kernel overcomes challenges such as data scarcity and reduces the computational cost substantially compared to previous state-of-the-art Bayesian optimization methods. The described method can be extended to study mixed ions and mixed halide/cation systems, and could be applicable to a wide range of materials design and optimization problems.