Login / Signup

Design Principles of Large Cation Incorporation in Halide Perovskites.

Heesoo ParkSyam KumarSanjay ChawlaFedwa El Mellouhi
Published in: Molecules (Basel, Switzerland) (2021)
Perovskites have stood out as excellent photoactive materials with high efficiencies and stabilities, achieved via cation mixing techniques. Overcoming challenges to the stabilization of Perovskite solar cells calls for the development of design principles of large cation incorporation in halide perovskite to accelerate the discovery of optimal stable compositions. Large fluorinated organic cations incorporation is an attractive method for enhancing the intrinsic stability of halide perovskites due to their high dipole moment and moisture-resistant nature. However, a fluorinated cation has a larger ionic size than its non-fluorinated counterpart, falling within the upper boundary of the mixed-cation incorporation. Here, we report on the intrinsic stability of mixed Methylammonium (MA) lead halides at different concentrations of large cation incorporation, namely, ehtylammonium (EA; [CH3CH2NH3]+) and 2-fluoroethylammonium (FEA; [CH2FCH2NH3]+). Density functional theory (DFT) calculations of the enthalpy of the mixing and analysis of the perovskite structural features enable us to narrow down the compositional search domain for EA and FEA cations around concentrations that preserve the perovskite structure while pointing towards the maximal stability. This work paves the way to developing design principles of a large cation mixture guided by data analysis of DFT data. Finally, we present the automated search of the minimum enthalpy of mixing by implementing Bayesian optimization over the compositional search domain. We introduce and validate an automated workflow designed to accelerate the compositional search, enabling researchers to cut down the computational expense and bias to search for optimal compositions.
Keyphrases
  • ionic liquid
  • room temperature
  • density functional theory
  • solar cells
  • perovskite solar cells
  • molecular dynamics
  • electronic health record
  • machine learning
  • deep learning
  • molecular docking
  • big data