Data-driven design of high-performance MASnxPb1-xI3 perovskite materials by machine learning and experimental realization.

The photovoltaic performance of perovskite solar cell is determined by multiple interrelated factors, such as perovskite compositions, electronic properties of each transport layer and fabrication parameters, which makes it rather challenging for optimization of device performances and discovery of underlying mechanisms. Here, we propose and realize a novel machine learning approach based on forward-reverse framework to establish the relationship between key parameters and photovoltaic performance in high-profile MASnxPb1-xI3 perovskite materials. The proposed method establishes the asymmetrically bowing relationship between band gap and Sn composition, which is precisely verified by our experiments. Based on the analysis of structural evolution and SHAP library, the rapid-change region and low-bandgap plateau region for small and large Sn composition are explained, respectively. By establishing the models for photovoltaic parameters of working photovoltaic devices, the deviation of short-circuit current and open-circuit voltage with band gap in defective-zone and low-bandgap-plateau regions from Shockley-Queisser theory is captured by our models, and the former is due to the deep-level traps formed by crystallographic distortion and the latter is due to the enhanced susceptibility by increased Sn4+ content. The more difficulty for hole extraction than electron is also concluded in the models and the prediction curve of power conversion efficiency is in a good agreement with Shockley-Queisser limit. With the help of search and optimization algorithms, an optimized Sn:Pb composition ratio near 0.6 is finally obtained for high-performance perovskite solar cells, then verified by our experiments. Our constructive method could also be applicable to other material optimization and efficient device development.

There is plenty of room at the top: generation of hot charge carriers and their applications in perovskite and other semiconductor-based optoelectronic devices.

Hot charge carriers (HC) are photoexcited electrons and holes that exist in nonequilibrium high-energy states of photoactive materials. Prolonged cooling time and rapid extraction are the current challenges for the development of future innovative HC-based optoelectronic devices, such as HC solar cells (HCSCs), hot energy transistors (HETs), HC photocatalytic reactors, and lasing devices. Based on a thorough analysis of the basic mechanisms of HC generation, thermalization, and cooling dynamics, this review outlines the various possible strategies to delay the HC cooling as well as to speed up their extraction. Various materials with slow cooling behavior, including perovskites and other semiconductors, are thoroughly presented. In addition, the opportunities for the generation of plasmon-induced HC through surface plasmon resonance and their technological applications in hybrid nanostructures are discussed in detail. By judiciously designing the plasmonic nanostructures, the light coupling into the photoactive layer and its optical absorption can be greatly enhanced as well as the successful conversion of incident photons to HC with tunable energies can also be realized. Finally, the future outlook of HC in optoelectronics is highlighted which will provide great insight to the research community.