Periodic Reporting for period 1 - THERMOH (Thermoelectricity in metal-organic perovskites: recycling waste energy heat as electricity)
Reporting period: 2022-01-01 to 2023-12-31
The project focuses on metal halide perovskites, with particular attention to lead-free compounds: a promising eco-friendly alternative for energy harvesting technologies, particularly in solar cells. Metal halide perovskites exhibit polymorphism with different phases coexisting at room temperature. The research delves into the thermodynamic stability between different structures, utilizing first-principles thermodynamics and exploring phase transitions. This research addresses the broader issue of sustainable and efficient energy solutions, emphasizing the importance of metal halide perovskites in achieving these goals. The overall objective is to understand and improve these materials' mechanical stability and practical deployment, contributing to the advancement of eco-friendly energy technologies, analyzing their thermal properties for potential application as thermoelectric devices.
Throughout the project, we focused on enhancing the theoretical understanding of CsSnI3, a metal halide perovskite. A significant breakthrough was achieved with the development of a novel method that integrates long-range interactions within machine learning force fields. This advancement was pivotal in accurately modeling the material's behavior. The research, as detailed in the paper, provided deeper insights into the polymorphism and thermodynamic stability of CsSnI3. These findings are vital for exploring its potential in eco-friendly solar cell applications. The results, emphasizing the importance of sustainable energy solutions, were disseminated at various scientific events, including Psi-K 2022, APS March Meeting 2023, Total Energy and Forces International Workshop, and the Advanced School on Quantum Espresso in Pavia, showcasing the project's significant progress and impact in the field of energy materials.
The project represents a significant advancement beyond the current state of the art in the field of energy materials. By developing a novel method that integrates long-range interactions within machine learning force fields for CsSnI3, it has opened new avenues for accurately simulating and understanding metal halide perovskites. The expected results by the end of the project include a comprehensive understanding of CsSnI3's properties and its potential application in eco-friendly solar cells. This research has substantial socio-economic impacts, offering a sustainable alternative for energy production. The wider societal implications include promoting environmental conservation and influencing future research and development in renewable energy technologies.