Great Influence of Organic Cation Motion on Charge Carrier Dynamics in Metal Halide Perovskite Unraveled by Unsupervised Machine Learning.
Yulong LiuRun LongWei-Hai FangPublished in: The journal of physical chemistry letters (2022)
Unsupervised machine learning combined with time-dependent density functional theory reveals the significant influence of organic cation on the charge carrier lifetime of FAPbI 3 (FA = HC(NH 2 ) 2 + ) by analyzing their mutual information (MI) between the geometric features and the nonadiabatic coupling (NAC) and bandgap. Analysis of MI values demonstrates that the NAC and bandgap are dominated by the orientation and shape of the inorganic octahedron because iodine and lead atoms are composed of the band edge states. Counterintuitively, the correlated motion promotes the contribution of the FA cation to the NAC; in particular, one type of FA rotation even supersedes the influence of the velocities of the lead and iodine atoms due to the enhanced hydrogen bond interaction. Our study demonstrates the importance of the correlated motion on the excited-state lifetimes of FAPbI 3 , which provides a guidance for optimizing the optoelectronic properties of metal halide perovskites.
Keyphrases
- machine learning
- solar cells
- density functional theory
- transcription factor
- molecular dynamics
- room temperature
- ionic liquid
- artificial intelligence
- big data
- perovskite solar cells
- high speed
- water soluble
- dual energy
- genome wide analysis
- deep learning
- magnetic resonance
- computed tomography
- magnetic resonance imaging
- health information
- high resolution
- high efficiency