Multi-Properties prediction of perovskite materials using Machine learning and Meta-Heuristic feature selection DOI
F J Kusuma, Eri Widianto, Wahyono Wahyono

и другие.

Solar Energy, Год журнала: 2024, Номер 286, С. 113189 - 113189

Опубликована: Дек. 12, 2024

Язык: Английский

Electronic transport properties of Rb2AsAuX6 (X = Cl, Br) halide double perovskites for energy harvesting applications DOI
Muhammad Adnan, Mudassir Ishfaq, Shatha A. Aldaghfag

и другие.

Chemical Physics Letters, Год журнала: 2024, Номер 857, С. 141733 - 141733

Опубликована: Ноя. 2, 2024

Язык: Английский

Процитировано

7
Enhancing Durability of Organic–Inorganic Hybrid Perovskite Solar Cells in High‐Temperature Environments: Exploring Thermal Stability, Molecular Structures, and AI Applications DOI

Shixuan Su,

Tae Kyu Ahn, Yun Yang

и другие.

Advanced Functional Materials, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 19, 2024

Abstract The commercialization of perovskite solar cells (PSCs), as an emerging industry, still faces competition from other renewable energy technologies in the market. It is essential to ensure that PSCs are durable and stable high‐temperature environments order meet varied market demands hot regions or seasons. influence high temperatures on complex, encompassing factors such lattice strain, crystal phase changes, creation defects, ion movement. Furthermore, it intensifies vibrations phonon scattering, which turn impacts migration rate charge carriers. This review focuses durability organic–inorganic hybrid under temperatures. begins by analyzing impact external temperature variations internal dynamics PSCs. Subsequently, outlines various mechanisms provided different functional molecules, applied interface stabilization, grain boundary passivation, growth control, electrode protection, development new hole transport layers, enhance thermal stability Additionally, machine learning (ML) discussed for predicting structure stability, operational material screening, with a focus potential deep explainable artifical intelligence (AI) techniques

Язык: Английский

Процитировано

5
Perovskite solar cells: Organic-based molecules for electron and hole transport materials with machine learning insights DOI
Reda M. El‐Shishtawy,

Nesma ElShishtawy

Current Opinion in Colloid & Interface Science, Год журнала: 2024, Номер 74, С. 101848 - 101848

Опубликована: Авг. 31, 2024

Язык: Английский

Процитировано

3
Electronic Characteristics of Layered Heterostructures Based on Graphene and Two-Dimensional Perovskites: First-Principle Study DOI Creative Commons
Leonid Zubkov, Pavel A. Kulyamin, К. С. Гришаков

и другие.

Colloids and Interfaces, Год журнала: 2025, Номер 9(2), С. 23 - 23

Опубликована: Апрель 10, 2025

Layered perovskites have been actively studied due to their outstanding electronic and optical properties as well kinetic stability. with hexagonal symmetry special properties, such the Dirac cone in band structure, similar graphene. In presented study, heterostructure of single-layer all-inorganic lead-free perovskite A3B2X9 type (A = Cs, Rb, K; B In, Sb; X Cl, Br) graphene (Gr) was studied. The structural characteristics A3B2X9/Gr composite were calculated using density functional theory. It found that is not deformed, while main deformation observed only perovskite. B-X bonds different sensitivities stretching or compression. Fermi level A3In2X9/Gr can be shifted down from point, which used create optoelectronic devices spacer layers for graphene-based resonant tunneling nanostructures.

Язык: Английский

Процитировано

0
Machine Learning-Assisted Analysis of Perovskite Solar Cell Long-Term Stability under Multiple Environmental Factors DOI
Shanshan Zhao,

Sijia Zhou,

Zhongli Guo

и другие.

ACS Sustainable Chemistry & Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 6, 2025

Язык: Английский

Процитировано

0
Machine learning-assisted SCAPS device simulation for photovoltaic parameters prediction of CsSnI3 perovskite solar cells DOI
I. Chabri,

Mithilesh Said,

Ed. El-Allaly

и другие.

Materials Today Communications, Год журнала: 2024, Номер unknown, С. 110585 - 110585

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

2
Machine Learning-Assisted Prediction of Ambient-Processed Perovskite Solar Cells’ Performances DOI Creative Commons
Dowon Pyun, Seungtae Lee, Solhee Lee

и другие.

Energies, Год журнала: 2024, Номер 17(23), С. 5998 - 5998

Опубликована: Ноя. 28, 2024

As we move towards the commercialization and upscaling of perovskite solar cells, it is essential to fabricate them in ambient environment rather than conventional glove box environment. The efficiency ambient-processed cells lags behind those fabricated controlled environments, primarily owing external environmental factors such as humidity temperature. In case device fabrication relying solely on a single parameter, temperature or humidity, insufficient for accurately characterizing conditions. Therefore, dew point introduced parameter which accounts both humidity. this study, machine learning model was developed predict based meteorological data, particularly point. A total 238 were fabricated, their photovoltaic parameters points collected from March December 2023. data used train various tree-based models, with random forest achieving highest accuracy. efficiencies January February 2024 predicted MAPE 4.44%. An additional Shapley Additive exPlanations analysis confirmed significance performance cells.

Язык: Английский

Процитировано

0
Multi-Properties prediction of perovskite materials using Machine learning and Meta-Heuristic feature selection DOI
F J Kusuma, Eri Widianto, Wahyono Wahyono

и другие.

Solar Energy, Год журнала: 2024, Номер 286, С. 113189 - 113189

Опубликована: Дек. 12, 2024

Язык: Английский

Процитировано

0