Feeding 9 Billion: Innovations in Agricultural Modeling
Global electricity consumption is rising at an unprecedented pace. According to the International Energy Agency, electricity is projected to account for more than 50% of global energy use within the next 25 years, compared to the current 20%. This creates a pressing need for sustainable, efficient energy conversion methods, particularly advanced solar technologies.
“To meet the demand, there is a significant 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 optimal utilisation. It’s very exciting that we now have simulation methods that can answer questions that were unresolved just a few years ago,” says Julia Wiktor, principal investigator and associate professor at Chalmers University of Technology.
Halide Perovskites: A Promising Material Class
Among the most promising candidates for next-generation solar cells are halide perovskites. These materials are cost-effective, lightweight, flexible, and highly efficient at absorbing and emitting light. They also hold potential for applications beyond solar energy, such as in LED lighting and other optoelectronic devices.
However, challenges remain. Halide perovskites are prone to rapid degradation, and one compound in particular—formamidinium lead iodide—offers excellent optoelectronic properties but suffers from instability. Researchers believe that stability can be improved by mixing different types of halide perovskites, but deeper insights are needed into their structural behavior.
Unlocking the Key to Material Design
A team at Chalmers University has now provided new knowledge about a low-temperature phase of formamidinium lead iodide that has long puzzled scientists. Their findings open the door to better control and design of both the pure compound and its mixtures. The research was recently published in the Journal of the American Chemical Society.
“The low-temperature phase of this material has long been a missing piece of the research puzzle and we’ve now settled a fundamental question about the structure of this phase,” says Sangita Dutta, researcher at Chalmers.
Machine Learning Accelerates Discovery
Modeling halide perovskites has been notoriously difficult because of their complex behaviors, requiring supercomputers and extensive simulation times. The Chalmers team overcame this by combining traditional simulation methods with machine learning, making it possible to run far longer simulations and model systems with millions of atoms—much closer to real-world conditions.
“By combining our standard methods with machine learning, we’re now able to run simulations that are thousands of times longer than before. And our models can now contain millions of atoms instead of hundreds, which brings them closer to the real world,” explains Dutta.
Bridging Simulations with Experiments
The researchers confirmed their models through collaboration with colleagues at the University of Birmingham, who cooled the material to -200°C. The experiments revealed that formamidinium molecules become trapped in semi-stable states during cooling, consistent with the simulated results.
“We hope the insights we’ve gained from the simulations can contribute to how to model and analyse complex halide perovskite materials in the future,” says Erik Fransson, Department of Physics, Chalmers.
Key Takeaways
- Electricity demand will surpass 50% of global energy use in 25 years.
- Halide perovskites are highly promising for solar cells but need better stability.
- Chalmers researchers clarified the long-debated low-temperature phase of formamidinium lead iodide.
- Machine learning allowed longer, larger-scale simulations for accurate modeling.
- Findings were experimentally validated in collaboration with the University of Birmingham.
Original Publication
Journal: Journal of the American Chemical Society
DOI: 10.1021/jacs.5c05265
Method of Research: Computational simulation/modeling
Subject of Research: Not applicable
Article Title: Revealing the Low-Temperature Phase of FAPbI3 Using a Machine-Learned Potential
Article Publication Date: 14-Aug-2025
COI Statement: The authors declare no competing financial interest.
Original Source: https://www.chalmers.se/en/
Frequently Asked Questions
What is the significance of the low-temperature phase in FAPbI3 perovskite solar cells?
The low-temperature phase of FAPbI3 is important because it shows a disordered structure that affects the material’s stability and performance. Understanding this phase helps clarify how the material behaves under different conditions, which is crucial for improving solar cell efficiency.
How do the dynamics of organic cations influence the properties of FAPbI3?
The dynamics of organic cations, like FA molecules, play a key role in determining the material’s phase stability and electronic properties. Their behavior can lead to kinetic trapping in less stable phases, which can affect the overall performance of the solar cells.
What role do machine-learned interatomic potentials play in studying FAPbI3?
Machine-learned interatomic potentials allow researchers to efficiently simulate the atomic-scale dynamics of FAPbI3, capturing complex behaviors without the high computational costs of traditional methods. This helps in accurately predicting the material’s phases and understanding its dynamics.
Most Read Articles
