Machine learning and first-principles insights on molecularly modified CH3NH3PbI3 film in water

Zhang, Lei; He, Mu; Hu, Wenguang; Ge, Hongfeng

doi:10.1016/j.apsusc.2022.153428

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Bader charge analysis suggests that 0.015, 0.026, and 0.017 electrons are transferred from the perovskite to SP1, SP2, and SP3, respectively, which facilitates the further transfer of electrons to the nearby electron transfer layer. 10 A built-in electric field is induced at the interface, which can be seen in the z -direction planar-averaged charge difference in Fig. 3(d).…”

Section: Resultsmentioning

confidence: 94%

“…The H–I interactions are non-negligible when molecules are adsorbed on the perovskite surface in a horizontal adsorption manner. 10 The S–Pb distances are 3.86, 3.88, and 3.84 Å in SP1@MAPbI 3 , SP2@MAPbI 3 , and SP3@MAPbI 3 , respectively. More additional interactions bring the PMs closer to the perovskite surface.…”

Section: Resultsmentioning

confidence: 94%

“…In addition, SP1 or SP2 introduces occupied states close to the conduction band, providing additional states for the holes and favoring the overall photocurrents. 10 In SP3@MAPbI 3 , a different scenario emerges. A trap state introduced by the SP3 molecule occurs near the valence band at À0.24 eV, which reduces the band gap of the system.…”

Section: Electronic Property Of Pm@mapbimentioning

confidence: 99%

“…9 In particular, water molecules easily penetrate into the surfaces of the perovskite, making the performance of the perovskite-based device significantly worse. 10 The industrialization of perovskite-based devices is constrained by the unsatisfactory stability of perovskite materials. 11 The defects on the surface and grain boundaries of perovskite materials are mainly represented by insufficient coordination of cations or halide anions, which causes non-radiative charge recombination.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative multiple interactions of donor–π–acceptor dyes enhance the efficiency and stability of perovskite solar cells

Hou

Zhang

2023

Phys. Chem. Chem. Phys.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 94%

Section: Electronic Property Of Pm@mapbimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cooperative multiple interactions of donor–π–acceptor dyes enhance the efficiency and stability of perovskite solar cells

Hou

Zhang

2023

Phys. Chem. Chem. Phys.

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“…Consequently, numerous research papers, including several reviews, have been published on ML applications of halide perovskites. Although most of the works published so far have focused on halide perovskites solar cells, the experience gained in the field will be also beneficial for PEC applications as well because the visible light harvesting efficiency and stability are also the major performance measures in PEC applications, which have also been presented in the scientific literature more frequently in recent years due to the progress achieved in the water resistance of halide perovskites. − …”

Section: Introductionmentioning

confidence: 99%

Halide Perovskites for Photoelectrochemical Water Splitting and CO₂ Reduction: Challenges and Opportunities

Bienkowski,

Solarska,

Trinh

et al. 2024

ACS Catal.

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Photoelectrochemical water splitting and CO2 reduction provide an attractive route to produce solar fuels while reducing the level of CO2 emissions. Metal halide perovskites (MHPs) have been extensively studied for this purpose in recent years due to their suitable optoelectronic properties. In this review, we survey the recent achievements in the field. After a brief introduction to photoelectrochemical (PEC) processes, we discussed the properties, synthesis, and application of MHPs in this context. We also survey the state-of-the-art findings regarding significant achievements in performance, and developments in addressing the major challenges of toxicity and instability toward water. Efforts have been made to replace the toxic Pb with less toxic materials like Sn, Ge, Sb, and Bi. The stability toward water has been also improved by using various methods such as compositional engineering, 2D/3D perovskite structures, surface passivation, the use of protective layers, and encapsulation. In the last part, considering the experience gained in photovoltaic applications, we provided our perspective for the future challenges and opportunities. We place special emphasis on the improvement of stability as the major challenge and the potential contribution of machine learning to identify the most suitable formulation for halide perovskites with desired properties.

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Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Liu

Tan

Liang

et al. 2023

Adv Funct Materials

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Data-driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability and efficiency to handle nonlinear and game-playing problems is unmatched by traditional simulation computing software and trial-error experiments. Perovskite solar cells are complex physicochemical devices (systems) that consist of perovskite materials, transport layer materials, and electrodes. Predicting the physicochemical properties and screening the component materials related to perovskite solar cells is the strong point of ML. However, the applications of ML in perovskite solar cells and component materials has only begun to boom in the last two years, so it is necessary to provide a review of the involved ML technologies, the application status, the facing urgent challenges and the development blueprint.

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Machine learning and first-principles insights on molecularly modified CH3NH3PbI3 film in water

Cited by 6 publications

References 35 publications

Cooperative multiple interactions of donor–π–acceptor dyes enhance the efficiency and stability of perovskite solar cells

Cooperative multiple interactions of donor–π–acceptor dyes enhance the efficiency and stability of perovskite solar cells

Halide Perovskites for Photoelectrochemical Water Splitting and CO₂ Reduction: Challenges and Opportunities

Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

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Machine learning and first-principles insights on molecularly modified CH3NH3PbI3 film in water

Cited by 6 publications

References 35 publications

Cooperative multiple interactions of donor–π–acceptor dyes enhance the efficiency and stability of perovskite solar cells

Cooperative multiple interactions of donor–π–acceptor dyes enhance the efficiency and stability of perovskite solar cells

Halide Perovskites for Photoelectrochemical Water Splitting and CO2 Reduction: Challenges and Opportunities

Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Contact Info

Product

Resources

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Halide Perovskites for Photoelectrochemical Water Splitting and CO₂ Reduction: Challenges and Opportunities