Computational and Experimental Insights into Electro- and Photocatalytic Materials for Sustainable Energy Conversion

Electro- and photocatalytic materials are central to enabling sustainable energy conversion processes such as water splitting, CO2 reduction, oxygen reduction, and ammonia synthesis. These reactions underpin technologies critical for a low-carbon future, including hydrogen production, artificial photosynthesis, fuel cells, and green ammonia synthesis. Despite significant progress, developing catalysts with high activity, selectivity, and stability remains a major scientific and technological challenge. Advances in computational materials science—particularly density functional theory (DFT), molecular dynamics (MD), multiscale modeling, and machine learning—are rapidly accelerating catalyst discovery by providing mechanistic insights and predictive design principles. At the same time, experimental techniques continue to improve our ability to synthesize, characterize, and test catalytic materials under realistic operating conditions. Integrating these computational and experimental approaches offers powerful opportunities to advance next-generation energy materials.This Research Topic aims to address the fundamental scientific challenges associated with designing efficient electro- and photocatalysts through a combined computational and experimental perspective. Catalytic processes involve complex interactions at surfaces, interfaces, defects, and reaction intermediates, making it difficult to identify active sites, predict reaction pathways, and optimize material properties. Recent developments in theory, simulation, and data-driven approaches offer unprecedented resolution for studying catalytic mechanisms and guiding material optimization. Similarly, state-of-the-art experimental methodologies, including in situ/operando spectroscopy, electron microscopy, and advanced electrochemical characterization, allow researchers to directly probe catalytic behavior under working conditions.By bringing together contributions from both communities, this Research Topic seeks to bridge the gap between computational predictions and experimental validation. The goal is to accelerate the development of design rules, performance descriptors, and reliable structure–property relationships that enable the discovery of robust, high-performance catalysts for sustainable energy conversion. We welcome studies that demonstrate how theory and experiment can complement each other to solve long-standing challenges in electrocatalysis and photocatalysis.This Research Topic invites Original Research, Reviews, Mini-reviews, and Perspective articles that focus on computational, experimental, or combined approaches to understanding and designing catalytic materials for sustainable energy applications. Areas of interest include, but are not limited to:• Computational modeling (DFT, MD, multiscale methods) of electro- and photocatalytic reactions for water splitting, CO2 reduction, ORR, and ammonia synthesis• Experimental synthesis and characterization of catalytic materials, including in situ/operando techniques• Mechanistic insights into reaction pathways, active sites, and structure–property relationships• Defect engineering, interfacial phenomena, charge transfer dynamics, and catalyst stability analysis• Machine learning and data-driven methods for catalyst discovery and performance prediction• Studies that demonstrate strong computational–experimental synergyWe encourage submissions from materials scientists, chemists, physicists, and chemical engineers working at the interface of theory and experiment. Manuscripts addressing emerging concepts, innovative modeling frameworks, or advanced characterization techniques are particularly welcome. Topic editors: Getasew Mulualem Zewdie, Mourad Boujnah, Yusuf Zuntu Abdullahi, Tesfaye Tadesse Gebremariam Source: https://www.frontiersin.org/research-topics/77117/computational-and-experimental-insights-into-electro--and-photocatalytic-materials-for-sustainable-energy-conversion