Interlayer Polarization Explains Slow Charge Recombination in Two-Dimensional Halide Perovskites by Nonadiabatic Molecular Dynamics Simulation

Spin–Orbit Coupling Is the Key to Promote Asynchronous Photoinduced Charge Transfer of Two-Dimensional Perovskites

“…However, there is no theoretical work reported that explores electronic structures and interfacial properties of the above-mentioned (4Tm) 2 PbI 4 . 23 , 28 , 47 − 49 …”

“…Structurally, 2D perovskites can be understood as thin slabs that are cut from 3D counterparts along certain crystal indices. These thin perovskite slabs can further be sandwiched by two-layer organic ligands, which finally forms quantum wells by periodically repeating organic and inorganic layers along the out-of-plane growth direction to provide flexible tunability for their structures and properties. − …”

“…In addition to static properties, dynamical properties were also extensively explored by the Prezhdo, Tretiak, Neukirch, Brédas, and Zhao groups who have studied photoinduced carrier dynamics and lifetimes, nonradiative electron–hole recombination of a series of 2D perovskites (Dion-Jacobson and Ruddlesden–Popper phases) with varying organic spacers, and perovskite compositions by means of time-domain nonadiabatic molecular dynamics simulations. However, there is no theoretical work reported that explores electronic structures and interfacial properties of the above-mentioned (4Tm) 2 PbI 4 . ,,− …”

Understanding the Mechanism of PbCl2 Additive for MAPbI3‐Based Perovskite Solar Cells

“…[ 11 ] Many studies have demonstrated that regulation of the photosensitive layers can effectively improve the photovoltaic efficiency of perovskite solar cells. [ 12–15 ] However, the physical mechanism underpinning photoelectric devices still requires further exploration and optimization to enhance photovoltaic performance. Charge accumulation at the absorber and electrode interfaces impedes the movement of charge carriers during the transportation of electron–hole pairs, leading to a significant loss in the PCE.…”

Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning

“…Recently, it has been demonstrated that supervised ML can be used to accelerate nonadiabatic (NA) molecular dynamics (MD) simulations − and that an increase in efficiency of ≤2 orders of magnitude can be realized by interpolation of NA Hamiltonians along NA-MD trajectories generated under the classical path approximation. − NA-MD modeling can provide key insights into excited state dynamics, whereby the Born–Oppenheimer approximation breaks down as the electronic and nuclear degrees of freedom cannot be adiabatically separated. , NA-MD simulations can directly yield macroscopic observables, such as quantum yield, without any prior knowledge of the mechanism, making it a particularly powerful tool for large systems with strong coupling, in which the choice of a reaction coordinate might be unfeasible or difficult. − However, NA-MD simulations typically require intensive ab initio calculations of geometry-dependent energies and forces for the different states and the NA coupling (NAC) between them. This drawback has been partially alleviated through the use of ML to predict bandgap and NAC from a small fraction of the data, significantly reducing the computational load for ab initio calculations. ,,,− …”