ABSTRACT The growing global imperative for sustainable energy solutions is catalysing a transformative shift in materials science, where nanostructured materials, endowed with unique quantum and surface‐dependent properties, have emerged as critical drivers of next‐generation energy technologies. However, the extraordinary complexity of their design space makes traditional experimental discovery methods impractically slow, hindering progress towards urgent energy goals. The convergence of Artificial Intelligence (AI) and Machine Learning (ML) is revolutionising this landscape: AI/ML algorithms excel at rapidly screening vast virtual material libraries, predicting properties with high accuracy (often exceeding 90% for specific properties such as bandgap or stability), optimising nanostructures for targeted functionalities, and uncovering novel compositions via inverse design approaches. This review critically evaluates that AI‐driven methodologies, leveraging advanced tools such as graph neural networks and generative models, can drastically accelerate the design‐build‐test‐learn cycle, which is essential for addressing pressing global energy challenges. This review critically evaluates how AI‐driven methodologies align with expert‐system frameworks in materials science, emphasising decision‐support systems and rule‐based learning for accelerated discovery. Current applications highlight significant strides in key domains, including enhanced stability of perovskite photovoltaics, development of high‐performance solid‐state battery electrolytes, efficient catalysts for CO 2 reduction, and improved thermoelectric materials. This review also addresses persistent challenges, such as data scarcity, model interpretability, and the need for experimental validation. While challenges persist, such as data scarcity and the need for greater model interpretability, advances in materials informatics infrastructure and algorithmic sophistication underscore immense potential. This perspective analyzes AI's pivotal role in advancing the full potential of nanomaterials for a sustainable energy future, calling for strengthened interdisciplinary collaboration and integrated AI‐driven development to accelerate innovation. It also discusses limitations and conflicting findings in the current literature, providing a balanced assessment of the field.
Alberto Boretti (Mon,) studied this question.