New insights into Halogenide Perovskites could transform solar cell technology
The worldwide electricity consumption is increasing rapidly and researchers at Chalmers University of Technology in Sweden have made progress in the direction of the development of the next generation of solar cell materials. Their work uses computer simulations and machine learning to better understand Halogenide Perovskites, which are lightweight, flexible and very efficient, but susceptible to instability.
According to the International Energy Agency, electricity will be good for more than half of the energy consumption in 25 years, an increase of 20 percent today. “To meet demand, there is a considerable and growing need for new, environmentally friendly and efficient energy conversion methods, such as more efficient solar cells.
“Our findings are essential to engineer and control one of the most promising solar cell materials for optimum use. It is very exciting that we now have simulation methods that can answer questions that have not been resolved a few years ago,” said Julia Wiktor, main investigator and university teacher at Chalmers.
Halide -perovskites are among the strongest candidates for future solar technologies and offer high efficiency of low costs and potential applications, ranging from building coatings to LEDs. Yet they quickly relegate and require a deeper scientific concept to improve stability.
A critical focus was Formamidinium lead iodide, a crystalline connection with excellent opto -electronic properties but limited use due to the instability. Researchers believe that mixing perovskiet types can solve the problem, but such approaches require precise knowledge of their phases and interactions.
The Chalmers team has now described the elusive phase low temperature of Formamidinium-Lood Jodide, providing missing information that is needed to design and control both this material and the mixtures. “The low temperature phase of this material has long been a missing piece of the research puzzle and we have now arranged a fundamental question about the structure of this phase,” said Chalmers-researcher Sangita Dutta.
By integrating machine learning with traditional modeling, the group extended thousands of times longer than before simulation times and scaled models from hundreds of atoms to millions. This progress made unprecedented accuracy possible, later confirmed by experiments at the University of Birmingham, where the material was cooled to -200OC to replicate simulation conditions.
“We hope that the insights we have gained from the simulations can contribute to modeling and analyzing complex halide -perovskiet materials in the future,” said Erik Fransson of the Department of Physics at Chalmers.
Research report:Unveiling the low temperature phase of FAPBI3 using a machine-learned potential
