2021
DOI: 10.1039/d0ta10098f
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Hierarchical computational screening of layered lead-free metal halide perovskites for optoelectronic applications

Abstract: Vertically stacking layered metal halide perovskites (MHPs) have emerged as promising semiconductors for optoelectronic applications due to their low cost, tunable band gaps, and excellent stability and solution processability. However,...

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Cited by 20 publications
(18 citation statements)
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“…To further understand the spacer cation engineering on the optoelectronic properties of 2D perovskites, the maximum PCE (η) of DPA- n and PNMA- n ( n = 1–3) has been predicted by a developed PCE model as follows: where FF is the fill factor, E loss is the loss-in-potential defined as the energy difference between the electron and hole quasi-Fermi levels, S ( E ) is the AM1.5 solar energy flux at the photon energy E , E g is the band gap of the 2D HLIP absorber, and a ( E ) is the absorption factor that can be defined as a ( E ) = 1 – e –2α( E ) L , where α­( E ) is the absorption coefficient and L is the thickness of the absorber. The details about the determination of these parameters are listed in the Supporting Information (Figure S6).…”
Section: Resultsmentioning
confidence: 99%
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“…To further understand the spacer cation engineering on the optoelectronic properties of 2D perovskites, the maximum PCE (η) of DPA- n and PNMA- n ( n = 1–3) has been predicted by a developed PCE model as follows: where FF is the fill factor, E loss is the loss-in-potential defined as the energy difference between the electron and hole quasi-Fermi levels, S ( E ) is the AM1.5 solar energy flux at the photon energy E , E g is the band gap of the 2D HLIP absorber, and a ( E ) is the absorption factor that can be defined as a ( E ) = 1 – e –2α( E ) L , where α­( E ) is the absorption coefficient and L is the thickness of the absorber. The details about the determination of these parameters are listed in the Supporting Information (Figure S6).…”
Section: Resultsmentioning
confidence: 99%
“…In this work, we performed high-throughput DFT calculations to reveal the role of spacer organic cations in the band alignments and optoelectronic properties of 2D hybrid lead iodine perovskites (HLIPs) with a general formula of A′ 2 (MA) n −1 Pb n I 3 n +1 . This method is widely confirmed as an efficient way in the guidance of experimental synthesis and device applications of a variety of hybrid metal halide perovskites. , We consider more than 30 spacer organic cations A′ that are incorporated in turn onto the 2D perovskite surfaces and the range of layer thickness n in 2D perovskite structures is from 1 to 3. These 2D structures with various spacer organic cations are screened by energetic and electronic parameters, including the decomposition energy, band gap, carrier effective mass, and band alignment.…”
Section: Introductionmentioning
confidence: 99%
“…One should underline that, as a step further, a more detailed analysis can be performed using computationally more demanding ab initio molecular dynamics (AIMD) simulations, which allow an indepth investigation of oxygen and water molecule interactions with the perovskite. 30…”
Section: Quasi-2d Perovskite Structures: Electronic Structure and Sta...mentioning
confidence: 99%
“…One should underline that, as a step further, a more detailed analysis can be performed using computationally more demanding ab initio molecular dynamics (AIMD) simulations, which allow an indepth investigation of oxygen and water molecule interactions with the perovskite. 30 A similar DFT based scheme for the calculation of optoelectronic and stability properties was employed in ref. 4 for 3D mixed-cation mixed-halide perovskite materials of general formula A x A 0 1Àx PbX y X 0 z X 00 3ÀyÀz , where A and A 0 are small size cations.…”
Section: Introductionmentioning
confidence: 99%
“…However, a much wider structural complexity of two-dimensional (2D) HHPs in comparison with 3D ones hampers the possibilities of a wide-scale application of the common DFT-based approaches for the prediction of new LHHPs. Therefore, the combined approaches based on DFT and ML have been successfully applied to find the most promising structures in terms of band gap, effective mass of charge carriers, stability, and environmental friendliness/low toxicity. ML has helped dramatically to accelerate and to scale up computational screening of LHHPs; , thus, discriminative models have already replaced partially the DFT calculations, since they link directly structural parameters of the materials with their properties. However, revealing the most reliable structure–property relationships of LHHPs remains complicated by relatively few available experimental structures suitable for machine learning approaches.…”
Section: Introductionmentioning
confidence: 99%