Artificial intelligence in nanotechnology: current applications and future perspectives

Engineering notes

Key topics: self-driving, reinforcement learning, large language model. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract The convergence of artificial intelligence (AI) and nanotechnology has substantially transformed the discovery, design, synthesis, characterization and biomedical application of nanomaterials. This review provides a structured overview of AI applications in nanotechnology, including data-driven nanomaterial discovery and inverse design using machine learning (ML) and generative models, optimization of nanoparticle synthesis through Bayesian optimization and self-driving laboratories, targeted drug delivery and personalized nanomedicine enabled by predictive ML models, deep learning for nanoscale imaging, spectroscopy and real-time particle tracking, AI-accelerated simulation of nanofluids and complex nanosystems, together with the emerging challenges, opportunities and future directions of AI-driven nanotechnology. We discuss the capabilities and limitations of widely used AI methods, including artificial neural networks, random forests, support vector machines, reinforcement learning, generative adversarial networks, variational autoencoders, graph neural networks and large language models, highlighting their suitability for different nanotechnology applications. In addition, the review examines key challenges that currently limit broader translation of AI-enabled nanotechnologies, including limited availability of standardized high-quality datasets, model interpretability, reproducibility, validation across independent datasets and regulatory considerations. Finally, we discuss emerging research directions, including autonomous experimentation, multiscale AI frameworks, AI-assisted nanorobotic systems and closed-loop therapeutic platforms, emphasizing that these represent promising future opportunities requiring further technological development and rigorous experimental and clinical validation.

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