| Abstract Scope |
Improving the environmental stability of halide perovskites is a critical challenge in perovskite solar cell development.1 Despite the remarkable photovoltaic performances,2 methylammonium lead iodide (MAPbI3) is notorious for its heat and moisture instability.3 Intensive research have been put into composition engineering in the past several years, where cation substitutions, e.g. incorporating alkaline metal Cs and small organic ion e.g. formamidinium (FA) into the MAPbI3 lattice, are shown to be among the most effective stabilization strategies.4 However, identifying and optimizing mixed-ion perovskites for reliability in real-world climates is a very challenging task due to the vast composition possibilities and the lack of physics-informed guidance. In this talk I will discuss our recent progress incorporating DFT into a Bayesian optimization algorithm5 to direct the search for novel semiconductors. To effectively design solar materials that are stable under the industrial standard of 85 RH% and 85°C reliability test, we combined the strengths of theory-guided and data-guided methodologies with in situ degradation tests, enabling a “smart search” strategy in a multi-parameter space. We took both calculations and experimental data into our machine-learning decision-making step, which have led to a significant acceleration in the search process. Validation on new materials are further achieved by an employment of the synchrotron-based high-throughput XRD measurement, where the degradation profiles are directly correlated to the underlying structural changes. This work sheds light on combining theory, machine-learning and high-throughput experimentation to accelerate the development of novel solar materials. |