Session

DescriptionThe growing demand for sustainable energy solutions and next-generation materials has propelled the need for innovative design paradigms in nanomaterials research. This discussion proposes to explore how data-driven approaches, particularly machine learning (ML) and multiscale simulations, can be employed to rationally design functional nanomaterials with tunable properties—focusing on amorphous disorder, spin-crossover (SCO) phenomena, and plasmonic nanostructures relevant to solar energy conversion and storage. This topic is timely and impactful, aligning with global goals in clean energy, materials informatics, and smart nanotechnology. The discussion aims to catalyze novel ideas and collaborative efforts across disciplines.