Discovery of Pb-free hybrid organic–inorganic 2D perovskites using a stepwise optimization strategy

Abstract The current status of 2D organic–inorganic hybrid perovskites for use in photovoltaic (PV) and light-emitting diode (LED) applications lags far behind their 3D counterparts. Here, we propose a computational strategy for discovering novel perovskites with as few computing resources as possible. A tandem optimization algorithm consisting of an elitism-reinforced nondominated sorting genetic algorithm (NSGA-II) and a multiobjective Bayesian optimization (MOBO) algorithm was used for density functional theory (DFT) calculations. The DFT-calculated band gap and effective mass were taken as objective functions to be optimized, and the constituent molecules and elements of a Ruddlesden–Popper (RP) structure (n = 2) were taken as decision variables. Fourteen previously unknown RP perovskite candidates for PV and LED applications were discovered as a result of the NSGA-II/MOBO algorithm. Thereafter, more accurate DFT calculations based on the HSE06 exchange correlation functional and ab initio molecular dynamics (AIMD) were conducted for the discovered 2D perovskites to ensure their validity.