Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Abstract Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator...

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Autors principals:	Ruben Millan-Solsona, Marti Checa, Spenser R. Brown, Amber N. Bible, Bernadeta Srijanto, Laura Wiggins, Sita Sirisha Madugula, Alice L. B. Pyne, Jennifer L. Morrell-Falvey, Scott Retterer, Rama K. Vasudevan, Liam Collins
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2026-03-01
Col·lecció:	Nature Communications
Accés en línia:	https://doi.org/10.1038/s41467-026-70421-3
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