Emerging frontiers in machine learning-assisted design of functional nanomaterials

Machine learning (ML) has facilitated the design and optimization of functional nanomaterials by accelerating the search of more complex material compositions and structures than traditional trial-and-error and first-principles methods allow. Large conscriptive bibliographies of this literature seem to ignore the vast chasm between high predictive metrics and the empirical realities of the world. To fill this gap, this review aimed to create, evaluate, and analyze the state-of-the-art on ML-based design of functional nanomaterials. This indicates a conceptual model that outlines a macro perspective of the entire life cycle starting from initiating the end-to-end operational flow through data creation, descriptor engineering, model validation, and deployment to production. Not only is this not just a report about such performance measures, but rather it critiques their validity as methodology comparisons; quantifying use criteria like energy, electronics, and biomedicine across methodologies (regression, deep learning (DL), generative models) against one another. The review identifies typical obstacles to improvement, the urge to shun information, the risk of overfitting and data information, and the disregard for the synthesizability restrictions in inverse design. The systematic review includes 75 peer-reviewed publications from 2021 to 2026 that relate to the research topic and focus on the design of functional nanomaterials assisted by ML in energy, electronics, biomedicine, and environmental research. Unlike in the previous descriptive surveys, the paper is a critical analysis of the strength of the methods and validation processes, and the shortcomings of synthesizability, and found notable gaps in the reproducibility, thermodynamic screening, and physics-informed integration.