Transformative strategies in photocatalyst design: merging computational methods and deep learning

Journal of Materials Informatics, Journal Year: 2024, Volume and Issue: 4(4)

Published: Dec. 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.

Language: Английский

Feasibility study of real-time virtual sensing for water quality parameters in river systems using synthetic data and deep learning models

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 125191 - 125191

Published: April 1, 2025

Language: Английский

Revolutionizing Cesium Monitoring in Seawater through Electrochemical Voltammetry and Machine Learning

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 484, P. 136558 - 136558

Published: Nov. 28, 2024

Language: Английский