Computational Approaches to Carbon-Based Quantum Nanomaterials and Their Applications in Energy Conversion and Storage

This Collection focuses on computational approaches to carbon-based quantum nanomaterials and their diverse applications in energy conversion and storage. With growing global demands for sustainable and efficient energy technologies, carbon-based nanomaterials—such as graphene, carbon dots, carbon nanotubes, and quantum-confined systems—have emerged as promising candidates due to their tenable electronic structures, high surface areas, and unique quantum properties. This collection aims to highlight recent advances in computational modelling, simulation, and theory that guide the design, optimisation, and mechanistic understanding of these materials. Topics of interest include, but are not limited to, electronic structure calculations, quantum transport, machine learning-assisted material discovery, interface engineering, and multiscale simulations related to batteries, supercapacitors, fuel cells, and photocatalysis. We welcome contributions that integrate theoretical insights with experimental validation or offer predictive frameworks for next-generation materials. This collection will serve as a platform for researchers to share developments and inspire future innovations in the energy field.

Keywords: Nanocatalyst, Naocatlyst, Ammonia synthesis, Ammonia decomposition, H2 production.