RESUMO
The lack of efficient discovery tools for advanced functional materials remains a major bottleneck to enabling advances in the next-generation energy, health, and sustainability technologies. One main factor contributing to this inefficiency is the large combinatorial space of materials (with respect to material compositions and processing conditions) that is typically redolent of such materials-centric applications. Searches of this large combinatorial space are often influenced by expert knowledge and clustered close to material configurations that are known to perform well, thus ignoring potentially high-performing candidates in unanticipated regions of the composition-space or processing protocol. Moreover, experimental characterization or first principles quantum mechanical calculations of all possible material candidates can be prohibitively expensive, making exhaustive approaches to determine the best candidates infeasible. As a result, there remains a need for the development of computational algorithms that can efficiently search a large parameter space for a given material application. Here, we introduce PAL 2.0, a method that combines a physics-based surrogate model with Bayesian optimization. The key contributing factor of our proposed framework is the ability to create a physics-based hypothesis using XGBoost and Neural Networks. This hypothesis provides a physics-based "prior" (or initial beliefs) to a Gaussian process model, which is then used to perform a search of the material design space. In this paper, we demonstrate the usefulness of our approach on three material test cases: (1) discovery of metal halide perovskites with desired photovoltaic properties, (2) design of metal halide perovskite-solvent pairs that produce the best solution-processed films and (3) design of organic thermoelectric semiconductors. Our results indicate that the novel PAL 2.0 approach outperforms other state-of-the-art methods in its efficiency to search the material design space for the optimal candidate. We also demonstrate the physics-based surrogate models constructed in PAL 2.0 have lower prediction errors for material compositions not seen by the model. To the best of our knowledge, there is no competing algorithm capable of this useful combination for materials discovery, especially those for which data are scarce.
RESUMO
We illustrate the critical importance of the energetics of cation-solvent versus cation-iodoplumbate interactions in determining the stability of ABX3 perovskite precursors in a dimethylformamide (DMF) solvent medium. We have shown, through a complementary suite of nuclear magnetic resonance (NMR) and computational studies, that Cs+ exhibits significantly different solvent vs iodoplumbate interactions compared to organic A+-site cations such as CH3NH3+ (MA+). Two NMR studies were conducted: 133Cs NMR analysis shows that Cs+ and MA+ compete for coordination with PbI3- in DMF. 207Pb NMR studies of PbI2 with cationic iodides show that perovskite-forming Cs+ (and, somewhat, Rb+) do not comport with the 207Pb chemical shift trend found for Li+, Na+, and K+. Three independent computational approaches (density functional theory (DFT), ab initio Molecular Dynamics (AIMD), and a polarizable force field within Molecular Dynamics) yielded strikingly similar results: Cs+ interacts more strongly with the PbI3- iodoplumbate than does MA+ in a polar solvent environment like DMF. The stronger energy preference for PbI3- coordination of Cs+ vs MA+ in DMF demonstrates that Cs+ is not simply a postcrystallization cation "fit" for the perovskite A+-site. Instead, it may facilitate preorganization of the framework precursor that eventually transforms into the crystalline perovskite structure.
