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An exploration of the “on-the-fly” nonadiabatic couplings (NACs) for nonradiative relaxation and recombination of excited states in 2D Dion–Jacobson (DJ) lead halide perovskites (LHPs) is accelerated by a machine learning approach. Specifically, ab initio molecular dynamics (AIMD) of nanostructures composed of heavy elements is performed with the use of machine-learning force-fields (MLFFs), as implemented in the Vienna Ab initio Simulation Package (VASP). The force field parametrization is established using on-the-fly learning, which continuously builds a force field using AIMD data. At each time step of the molecular dynamics (MD) simulation, the total energy and forces are predicted based on the MLFF and if the Bayesian error estimate exceeds a threshold, an ab initio calculation is performed, which is used to construct a new force field. Model training of MLFF and evaluation were performed for a range of DJ-LHP models of different thicknesses and halide compositions. The MLFF-MD trajectories were evaluated against pure AIMD trajectories to assess the level of discrepancy and error accumulation. To examine the practical effectiveness of this approach, we have used the MLFF-based MD trajectories to compute NAC and excited-state dynamics. At each stage, results based on machine learning are compared to traditional ab initio based electronic dissipative dynamics. We find that MLFF-MD provides comparable results to AIMDs when MLFF is trained in an NPT ensemble. 

Two-dimensional organic–inorganic hybrid lead halide perovskites are of interest for photovoltaic and light emitting devices due to their favorable properties that can be tuned. Here we use density functional theory to model two-dimensional lead halide perovskites of different thicknesses and using two different hallogens. Excited-state optoelectronic properties of the perovskite models are examined using excited-state dynamics treated by reduced density matrix method. Nonadiabatic couplings were computed based on the on-the-fly approach along a molecular dynamics trajectory at ambient temperatures. The density matrix-based equation of motion for electronic degrees of freedom was used to determine the dynamics of electronic degrees of freedom. We observe that the thickness of the perovskite layer shows a redshift in the absorption spectra with increasing thickness but has minimal effect on the photoluminescence quantum yield of the material. 

Lead halide perovskites (LHP) are of interest for light-emitting applications due to the tunability of their bandgap across the visible and near-infrared spectrum (IR) coupled with efficient photoluminescence quantum yields (PLQY). It is widely speculated that photoexcited electrons and holes spatially separate into large negative (electron) and positive (hole) polarons. Polarons are expected to be optically active. With the observed optoelectronic signatures expecting to show potential excited states within the polaronic potential well. From the polaron excited-state we predict that large polarons should be capable of spontaneous emission, photoluminescence, in the mid-IR to far-IR regime based on the concept of inverse occupations within the polaron potential well. Here we use density functional theory (DFT), including spin–orbit coupling interactions, for calculations on a two-dimensional Dion-Jacobson (DJ) lead chloride perovskite atomistic model of various sizes as a host material for either negative or positive polarons to examine the effects of size on polaron formation. This work provides computational evidence that polaron formation through selective charge injection does not show the same level of localisation for positive and negative polarons.