Leveraging AI Innovation in Nanomaterials, Synthesis, Characterization, and Device Design

Abstract

Artificial intelligence (AI)-driven innovations in nanomaterials are reshaping traditional research paradigms, enabling accelerated discovery and design. This chapter reviews recent progress and emerging pathways in the convergence of AI and nanomaterials science, highlighting their synergistic potential in advancing the development of novel materials. Key areas of focus include leveraging existing and new experimental datasets to elucidate structure–property relationships, improving synthesis processes by employing AI-based optimization strategies, and integrating AI with advanced characterization techniques to extract critical features for iterative materials design improvement. Several case studies illustrate how AI enhances predictive modeling, synthesis control, and property characterization across diverse classes of nanomaterials. For user-defined applications, AI facilitates reverse engineering, shifting nanomaterials research from a trial-and-error methodology toward a predictive, intelligent, and application-oriented framework. The chapter concludes with a glimpse of some forward-looking state-of-the-art and niche applications, as this convergence leads to the Fourth Science Paradigm of data-exhaustive discovery, while also addressing the ethical considerations associated with integrating AI into this rapidly evolving field.

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