Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
This enables more efficient parameter extraction for perovskite materials, accelerating discovery in photovoltaics and optoelectronics, though it is incremental as it applies an existing optimization method to a specific domain.
The researchers tackled the challenge of extracting material parameters from perovskite experiments by developing a Bayesian optimization platform that can extract up to 8 parameters from transient photoluminescence data, revealing that thermal degradation reduces carrier mobility and trap-assisted recombination coefficients.
The ability to extract material parameters of perovskite from quantitative experimental analysis is essential for rational design of photovoltaic and optoelectronic applications. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters for perovskite. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experiment, based on a complex full physics model that includes drift-diffusion of carriers and dynamic defect occupation. An example study of thermal degradation reveals that the carrier mobility and trap-assisted recombination coefficient are reduced noticeably, while the defect energy level remains nearly unchanged. The reduced carrier mobility can dominate the overall effect on thermal degradation of perovskite solar cells by reducing the fill factor, despite the opposite effect of the reduced trap-assisted recombination coefficient on increasing the fill factor. In future, this platform can be conveniently applied to other experiments or to combinations of experiments, accelerating materials discovery and optimization of semiconductor materials for photovoltaics and other applications.