Carbon, Год журнала: 2024, Номер 230, С. 119626 - 119626
Опубликована: Сен. 11, 2024
Язык: Английский
Carbon, Год журнала: 2024, Номер 230, С. 119626 - 119626
Опубликована: Сен. 11, 2024
Язык: Английский
Progress in Materials Science, Год журнала: 2025, Номер unknown, С. 101433 - 101433
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
6Journal of Materials Informatics, Год журнала: 2024, Номер 4(4)
Опубликована: Дек. 31, 2024
Photocatalysis is a unique technology that harnesses solar energy through in-situ processes, operating without the need for external inputs. It integral to advancing environmental, energy, chemical, and carbon-neutral objectives, promoting dual goals of pollution control carbon reduction. However, conventional approach photocatalyst design faces challenges such as inefficiency, high costs, low success rates, highlighting integrating modern technologies seeking new paradigms. Here, we demonstrate comprehensive overview transformative strategies in design, combining computational materials science with deep learning technologies. The review covers fundamental principles followed by examination methods workflow deep-learning-assisted design. Deep approaches are extensively reviewed, focusing on discovery novel photocatalysts, microstructure property optimization, approaches, application exploration, mechanistic insights into photocatalysis. Finally, highlight synergy between multidimensional computation learning, while discussing future directions development. This offers summary offering not only enhance development photocatalytic but also expand practical applications photocatalysis various domains.
Язык: Английский
Процитировано
8Physical Review Materials, Год журнала: 2025, Номер 9(2)
Опубликована: Фев. 25, 2025
Язык: Английский
Процитировано
1ACS Catalysis, Год журнала: 2025, Номер unknown, С. 6690 - 6701
Опубликована: Апрель 10, 2025
Although there has been progress in designing organic photocatalysts, identifying and structurally distinct polymeric or molecular photocatalysts with high performance is still challenging. Using the properties of a set well-known polymer we performed virtual screening large data around 50 000 semiconductors. In initial stage, looked for candidates electronic similar to those best-performing photocatalysts. Next, screened using reactivity descriptors based on mechanisms derived from quantum chemical calculations selected cases. We identified 33 potential as hydrogen evolution reaction.
Язык: Английский
Процитировано
0The Journal of Physical Chemistry C, Год журнала: 2025, Номер unknown
Опубликована: Май 21, 2025
Язык: Английский
Процитировано
0Carbon, Год журнала: 2024, Номер 230, С. 119626 - 119626
Опубликована: Сен. 11, 2024
Язык: Английский
Процитировано
1