Artificial intelligence and machine learning in functional nanomaterials: From materials discovery to industrial applications

Authors

https://doi.org/10.48314/nna.vi.70

Abstract

Artificial Intelligence (AI), particularly Machine Learning (ML), has emerged as a transformative technology in nanomaterials research by enabling data-driven materials discovery, design, characterization, and optimization. The integration of ML with experimental techniques, computational modeling, and materials databases has significantly accelerated the development of advanced nanomaterials while reducing the time, cost, and complexity associated with conventional trial-and-error approaches. This review provides a comprehensive overview of recent advances in ML applications across the entire nanomaterial development pipeline. Fundamental ML paradigms, including supervised, unsupervised, reinforcement, and Deep Learning (DL), are first introduced, followed by their applications in nanomaterial discovery, property prediction, and synthesis optimization. Emerging AI-driven strategies such as High-Throughput Screening (HTS), inverse design, Graph Neural Networks (GNNs), and generative AI are also discussed as promising approaches for accelerating the development of next-generation functional nanomaterials. Furthermore, recent progress in AI-assisted nanomaterial characterization, energy-related nanomaterials, environmental nanotechnology, nanoelectronics, smart sensors, and intelligent manufacturing is critically reviewed. The current challenges associated with data availability, model interpretability, generalization capability, and experimental validation are also highlighted, together with future research directions involving explainable AI, autonomous laboratories, digital twins, and foundation models. This review demonstrates that ML has evolved from a predictive computational tool into a key enabling technology for intelligent nanomaterials research. Continued advances in algorithms, data infrastructure, and interdisciplinary collaboration are expected to accelerate the development of sustainable, high-performance nanomaterials for diverse scientific, industrial, energy, and environmental applications.

Keywords:

Machine learning, Artificial intelligence, Nanomaterials, Materials informatics, Property prediction, Autonomous materials discovery, Generative artificial intelligence

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Published

2026-09-10

Issue

Vol. 1 No. 3 (2026): Nano Nexus & Applications

Section

Articles

How to Cite

Pourqasem, J. . (2026). Artificial intelligence and machine learning in functional nanomaterials: From materials discovery to industrial applications. Nano Nexus & Applications, 1(3), 178-198. https://doi.org/10.48314/nna.vi.70