π Learning: A Performance‐Informed Framework for Microstructural Electrode Design (Adv. Energy Mater. 17/2023)
Source abstract
Abstract
High-Performance Fuel Cell Electrodes In article number 2300244, Zhiqiang Niu, Valerie J. Pinfield, Jin Xuan, and co-workers demonstrate an innovative performance-informed deep learning framework to enable the efficient and rational design and optimization of high-performance fuel cell electrodes. The proposed framework is successfully applied to two popular electrode design philosophies, i.e., forward and inverse design.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Analytical Chemistry and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Aeronautical and Automotive Engineering Loughborough University Loughborough LE11 3TU UK
College of Aeronautical Engineering Civil Aviation University of China Tianjin 300300 P. R. China
Dyson School of Design Engineering Imperial College London London SW7 2BX UK
Department of Mechanical Engineering Imperial College London London SW7 2BX UK
Department of Chemical Engineering Loughborough University Loughborough LE11 3TU UK
Department of Chemical and Process Engineering University of Surrey Surrey GU2 7XH UK
References
Reference metadata was not supplied for this record.