The inherent chemical tunability of perovskite materials has spurred extensive research into composition engineering within the perovskite community. However, identifying the optimal composition across a broad range of variations still remains a significant challenge. Conventional trial-and-error methods are prohibitively expensive and environmentally taxing for comprehensive screening. Here, we employed machine learning-accelerated atomic simulation to guide the design of stable perovskite solar cells absorbers.
Read more in our recent publication at JACS Au:
Machine Learning-Driven Insights for Phase-Stable FAxCs1–xPb(IyBr1–y)3 Perovskites in Tandem Solar Cells
JACS Au: Machine Learning-Driven Insights for Phase-Stable FAxCs1–xPb(IyBr1–y)3 Perovskites in Tandem Solar Cells
Posted in Research.