The global shift to sustainable energy necessitates advancements in material science, especially at the nanoscale level. The intersection of materials physics, nanotechnology, clean energy, and artificial intelligence (AI) has recently emerged as a significant area of
scientific inquiry. AI-driven data analytics, machine learning (ML), and predictive modeling are now enabling unprecedented capabilities in the design, optimization, and real-time monitoring of functional nanomaterials. This integration has created an urgent demand for
new platforms to disseminate cutting-edge discoveries in functional nanomaterials, AI-assisted materials design, and advanced processing methods.

This Research Topic aims to enhance the understanding of innovative strategies for addressing energy challenges via material innovation and AI-empowered modeling, while also examining the fundamental physics that dictate these materials' behavior in operational
and extreme conditions, such as irradiation environments.

We invite contributions that consolidate recent advancements in the design, synthesis, AI-driven discovery, and application of functional nanomaterials for energy harvesting, storage, and conversion technologies. Emphasis will be placed on hybrid composite systems,
micro/nanostructures, advanced thin-film coatings, and intelligent optimization frameworks that enhance device-level performance in supercapacitors, batteries, and energy harvesters, including piezoelectric, thermoelectric, photovoltaic, and triboelectric systems.

Topics of interest include, but are not limited to:
• Hybrid nanocomposites
• Thin-film technologies
• MEMS/NEMS platforms
• Smart coatings
• Ion-beam and SHI irradiation-based material modification
• Computational modeling and AI/ML-assisted materials design
• Smart Nanomaterials: AI and Physics for Next-Generation Energy Conversion and Storage
• Data-driven approaches for accelerated material discovery and scale-up
• Digital twins for nanomaterials and energy conversion systems

Submissions are encouraged to focus on novel approaches that not only improve device-level performance but also embrace AI-powered models for efficient, scalable, and intelligent energy solutions.

We encourage Original Research, Reviews, Mini Reviews, and
Perspectives providing experimental, theoretical, simulation-based, or AI-enabled insights. Interdisciplinary studies bridging physics, nanotechnology, AI, and energy materials science
are especially welcome.

Are you interested in contributing a manuscript to this Research Topic?

Bridging Materials Physics and Artificial Intelligence to Accelerate Breakthroughs in Sustainable Energy Technologies