Nontechnical Description
The sun represents the most abundant potential source of pollution-free energy on earth. Solar cells for producing electricity require materials that absorb the sun?s energy and convert its photons to electrons, a process called photovoltaics. The search for the best photovoltaic material is an active area of solar cell research. Recently, an exciting new class of photovoltaic materials called organo-metal perovskites has emerged. These materials are promising because they have low production cost, use elements and materials abundant in the earth?s crust, and currently possess solar energy conversion efficiencies of over 15%. Further research is needed to tune the optical properties of these materials to improve light absorption needed to push the efficiencies over 20% to be competitive with silicon solar cells, and to remove lead, which is toxic, from the material matrix. The goal of this project is to gain a fundamental understanding of the beneficial electronic and optical properties of organo-metal perovskites to address these two issues. Advanced computational approaches will be used to predict electronic and optical properties of organo-metal perovskites at the atomic level to study how quantum mechanics plays a role in the performance of these materials for solar photovoltaic applications. Model simulations will be performed to identify replacements for lead that maintain desirable solar energy absorption characteristics. Scientific results will be made available to the public in blog-style updates, and a database will be provided to the scientific community to confirm the findings and make use of this information. In addition, in this project will provide training of material science graduate and undergraduate students in advanced supercomputer techniques needed to provide a pipeline of trained scientists in this critical area of national workforce need.
Recently, organo-metal perovskites, especially those based on lead, have emerged as an exciting new class of earth-abundant photovoltaic materials with solar energy conversion efficiencies exceeding 15% and potential for low manufacturing cost. The goal of this project is to gain a fundamental understanding of the excitonic and optical properties of organo-metal perovskites through advanced computational models, and then use this approach to identify replacements for lead in the material matrix. The perovskite material system offers a large phase space for improvement by replacing different constituent atoms. However, for targeted development, a fundamental understanding of electronic correlations, particularly the physics that governs the excitonic and charge-transport properties, is needed. In this regard, the interplay between free carriers in systems with perovskite crystal structures that possess optical and excitonic properties is unknown. Theoretical spectroscopy techniques based on many-body perturbation theory will be used to achieve a fundamental understanding of light-matter interaction in perovskite-halide materials with different constituent ions. First-principles techniques based on Hedin?s approximation and the Bethe-Salpeter equation, which accurately computes quantum-mechanical, electron-electron, and electron-hole interactions, will be used to predict band gaps, effective carrier masses, and optical absorption spectra. By taking into account quasi-particle energies, excitonic effects, and the interplay with free carriers, this approach will provide mechanistic insights into the optical absorption process in perovskites. This project will also investigate the influence of different constituents, particularly systems with and without lead ions, on these optical absorption processes. The research outcomes will be made available to the public in blog-style updates, and the computational results will be provided to the scientific community for continued validation. In addition, in this project will provide training of material science graduate and undergraduate students in advanced atomistic modeling techniques on supercomputing platforms.
