AI and Machine Learning: Revolutionizing Nanomaterial Research Through Synthesis, Characterization, and Applications

The rapidly growing field of materials science is moving from traditional methods to new and innovative methodologies centered on small-scale materials. In particular, materials with dimensions ranging from 1–100 nm have unique properties because of the higher number of surface atoms. Such materials have also become instrumental in transforming the electronics, medicine, and energy industries. This review also covers the different classes of materials, the methods to synthesize and characterize them, the applications, and focuses on the role of AI and ML technologies in the progress of research on nanomaterials. The methods available in the synthesis of nanomaterials can be categorized into three different approaches: physical, chemical, and biological, and the applicability of the resulting nanomaterials will depend on the chosen approach. The classic methods have been modified and adapted in order to take nanomaterials challenges and opportunities into account to achieve control over the desired size, shape and, functionality. To analyze the structure, elemental composition, and behavior of nanomaterials, there are certain characterization techniques such as transmission electron microscopy, scanning electron microscopy, UV spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction, which play a key role in characterizing the physical and chemical properties of these materials. AI and trained ML are being used to optimize the design of materials and characterize the materials through image processing and data analysis. These data-centric approaches facilitate the discovery of new materials and improve the efficiency of characterization as well as predictive modeling to solve issues of stability and performance. Interdisciplinary collaborative work, incorporating AI and ML streamlining to safely and precisely address the design and synthesis challenges of nanomaterials. Overall, ML and AI are integral to advancing nanomaterial technologies to solve emerging challenges and to further material science into new frontiers. The focus of this research is the growing importance of AI and ML in providing solutions for global challenges and advancing nanomaterial research and its numerous mono and multidisciplinary consequences.