Scanning probe microscopy (SPM) is a powerful technique that enables the characterization and manipulation of materials at the atomic and molecular levels. However, conventional SPM techniques are limited by labor-intensive, subjective, and time-consuming measurements and analyses that heavily depend on operator expertise. Artificial intelligence (AI) offers a transformative solution to these challenges, with its robust data processing capabilities and significant potential for automating complex tasks. Integrating AI with SPM techniques promises to overcome many of the limitations of traditional approaches and unlock new opportunities for nanoscale research. In this review article, we provide an overview of recent developments in AI-empowered SPM, including applications in autonomous tip conditioning and functionalization, image analysis ranging from single molecules to mesoscopic length scales, atomic and molecular manipulation with improved efficiency, probe-assisted molecular reactions with single-bond precision, and the development of self-driving laboratories for synthesis optimization in conjunction with SPM characterization. Collectively, these advancements broaden the functionality and capabilities of SPM, enabling more intricate and quantitative investigations. We also discuss future perspectives for AI-empowered SPM, highlighting its potential to advance materials science research and drive technological innovation.
Li et al. (Sun,) studied this question.