Machine Learning for Organic Photovoltaic Polymers: A Minireview

Chinese Journal of Polymer Science, Год журнала: 2022, Номер 40(8), С. 870 - 876

Опубликована: Июнь 21, 2022

Язык: Английский

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Investigation of photovoltaic performance of lead-free CsSnI3-based perovskite solar cell with different hole transport layers: First Principle Calculations and SCAPS-1D Analysis

Solar Energy, Год журнала: 2022, Номер 249, С. 163 - 173

Опубликована: Дек. 1, 2022

Язык: Английский

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Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

Physical Chemistry Chemical Physics, Год журнала: 2023, Номер 25(15), С. 10417 - 10426

Опубликована: Янв. 1, 2023

Solubility plays a critical role in many aspects of research (drugs to materials). parameters are very useful for selecting appropriate solvents/non-solvents various applications. In the present study, Hansen solubility predicted using machine learning. More than 40 models tried search best model. Molecular descriptors and fingerprints used as inputs get comparative view. Machine learning trained molecular have shown higher prediction ability model fingerprints. their potential be easy fast compared density functional theory (DFT)/thermodynamic approach. creates "black box" connection properties. Therefore, minimal computational cost is required. With help best-trained model, green solvents selected small molecule donors that organic solar cells. Our introduced framework can select cells an way.

Язык: Английский

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Molecular level understanding of the chalcogen atom effect on chalcogen-based polymers through electrostatic potential, non-covalent interactions, excited state behaviour, and radial distribution function

Polymer Chemistry, Год журнала: 2022, Номер 13(42), С. 5993 - 6001

Опубликована: Янв. 1, 2022

Multi-dimensional modelling was used to study the effect of chalcogen atoms on non-covalent interactions, structural and electronic properties polymer materials. Their bulk were also studied at molecular level.

Язык: Английский

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Theoretical framework for achieving high V_oc in non-fused non-fullerene terthiophene-based end-capped modified derivatives for potential applications in organic photovoltaics

RSC Advances, Год журнала: 2023, Номер 13(11), С. 7535 - 7553

Опубликована: Янв. 1, 2023

Non-fused ring-based OSCs are an excellent choice, which is attributed to their low cost and flexibility in applications. However, developing efficient stable non-fused still a big challenge. In this work, with the intent increase Voc for enhanced performance, seven new molecules derived from pre-existing A-D-A type A3T-5 molecule proposed. Different important optical, electronic efficiency-related attributes of studied using DFT approach. It discovered that newly devised possess optimum features required construct proficient OSCs. They small band gap ranging 2.22-2.29 eV planar geometries. Six proposed have less excitation energy, higher absorption coefficient dipole moment than both gaseous solvent phases. The A3T-7 exhibited maximum improvement optoelectronic properties showing highest λmax at 697 nm lowest Ex 1.77 eV. lower ionization potential values, reorganization energies electrons interaction coefficients molecule. six developed (Voc 1.46-1.72 eV) = 1.55 eV). Similarly, almost all except W6 fill factor compared reference. This remarkable efficiency-associated parameters FF) proves these can be successfully used as advanced version terthiophene-based future.

Язык: Английский

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AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

ACS Nano, Год журнала: 2023, Номер 17(11), С. 9763 - 9792

Опубликована: Июнь 2, 2023

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth data, adoption exploitation of artificial intelligence (AI) as part materials research framework had tremendous impact on development nanomaterials. AI has enabled revolutionary next-generation paradigms significantly accelerate all stages material discovery facilitate exploration enormous design space. In this review, we summarize recent advancements applications discovery, with special emphasis selected nanotechnology net-zero future including solar cells, hydrogen energy, battery renewable CO2 capture conversion capture, utilization storage (CCUS) addition, discuss limitations challenges current area by identifying gaps that exist development. Finally, present prospect directions order large-scale

Язык: Английский

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A bibliometric analysis of machine learning techniques in photovoltaic cells and solar energy (2014–2022)

Energy Reports, Год журнала: 2024, Номер 11, С. 2768 - 2779

Опубликована: Фев. 22, 2024

Solar energy presents a promising solution to replace fossil-based sources, mitigating global warming and climate change. However, solar faces socio-economic, environmental, technical challenges. Computational tools like machine learning offer solutions these Despite numerous studies, there's lack of comprehensive research on ML applications in Photovoltaics Energy. This study conducts critical analysis Energy using publication trends bibliometric analysis, employing the PRISMA approach Scopus database. Results reveal high output, citations, international collaboration. Notable researchers include G. E. Georghiou Haibo Ma, with Ministry Education (China) being prolific affiliation. China emerges as most active nation due funding programs National Natural Science Foundation Key Research Development Program. contributes terms providing an patterns from 2014 2022, including topic categories important metrics, at levels country, institution, organisation. Analysing author-keyword data aggregate publishing themes identify influential journals. Enhancing comprehension hotspots focal points research. also aims discuss role Cognitive Computing cancer/tumor oncological research, emphasising potential for significant advancements obstacles that need be overcome order fully utilise its advantages. Future studies could extensive into cybersecurity systems particularly wake malware, phishing, other intrusion attacks grid infrastructure worldwide.

Язык: Английский

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Chemical similarity-based design of materials for organic solar cells: Visualizing the generated chemical space of polymers

Materials Today Communications, Год журнала: 2024, Номер 38, С. 108403 - 108403

Опубликована: Фев. 17, 2024

Язык: Английский

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Machine‐Learning Analysis of Small‐Molecule Donors for Fullerene Based Organic Solar Cells

Energy Technology, Год журнала: 2022, Номер 10(5)

Опубликована: Март 27, 2022

In recent years, development in organic solar cells speeds up and performance continuously increases. From the last few machine learning gains fame among scientists who are researching on cells. Herein, is used to screen small‐molecule donors for Molecular descriptors as input train models. A variety of machine‐learning models tested find suitable one. Random forest model shows best predictive capability (Pearson's coefficient = 0.93). New also designed from easily synthesizable building units. Their power conversion efficiencies (PCEs) predicted. Potential candidates with PCE > 11% selected. The approach presented herein helps select efficient materials short time ease.

Язык: Английский

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Learning from Fullerenes and Predicting for Y6: Machine Learning and High‐Throughput Screening of Small Molecule Donors for Organic Solar Cells

Energy Technology, Год журнала: 2022, Номер 10(6)

Опубликована: Апрель 1, 2022

In recent years, research on the development of organic solar cells has increased significantly. For last few machine learning (ML) been gaining attention scientific community working cells. Herein, ML is used to screen small molecule donors for models are fed by molecular descriptors. Various employed. The predictive capability a support vector found be higher (Pearson's coefficient = 0.75). best with fullerene acceptors selected pair Y6. New also designed taking into account quantum chemistry principles, using building units that searched through similarity analysis. Their energy levels and power conversion efficiencies (PCEs) predicted. Efficient PCE > 13% selected. This design discovery pipeline provides an easy fast way select potential candidates experimental work.

Язык: Английский

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