Ag–Bi Charge Redistribution Creates Deep Traps in Defective Cs2AgBiBr6: Machine Learning Analysis of Density Functional Theory

The Journal of Physical Chemistry Letters. 2022. Vol. 13. No. 16. P. 3645–3651.

Liu D., Perez C. M., Vasenko A., Prezhdo O. V.

Lead-free double perovskites hold promise for stable and environmentally benign solar cells; however, they exhibit low efficiencies because defects act as charge recombination centers. Identifying trap-assisted loss mechanisms and developing defect passivation strategies constitute an urgent goal. Applying unsupervised machine learning to density functional theory and nonadiabatic molecular dynamics, we demonstrate that negatively charged Br vacancies in Cs2AgBiBr6 create deep hole traps through charge redistribution between the adjacent Ag and Bi atoms. Vacancy electrons are first accepted by Bi and then shared with Ag, as the trap transforms from shallow to deep. Subsequent charge losses are promoted by Ag and Bi motions perpendicular to rather than along the Ag–Bi axis, as can be expected. In contrast, charge recombination in pristine Cs2AgBiBr6 correlates most with displacements of Cs atoms and Br–Br–Br angles. Doping with In to replace Ag at the vacancy maintains the electrons at Bi and keeps the trap shallow.

Keywords: машинное обучение, solar cells, Теория функционала плотности, солнечные элементы, перовскиты, Perovskites, Density functional theory (DFT), Machine Learning Analysis

Publication based on the results of: Experimental and theoretical studies of quantum effects in low-dimensional systems (2022)