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Life sciences · Preprint
arXiv · August 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint describes a novel closed-loop machine-learning workflow that autonomously selects both measurement location and experimental modality in scanning probe microscopy. The method is demonstrated on a single composition-spread AlScN wafer using tapping-mode and DART measurements, showing feasibility of combining rapid and slower measurements without exhaustive mapping. The work is not peer-reviewed and lacks validation across materials or comparison to standard approaches.
Proof-of-concept instrumental method development study. Composition-spread AlScN wafer for materials characterization. Intervention: Multitask Gaussian process-driven autonomous selection of measurement location and modality (tapping-mode or DART).
Multitask Gaussian process learns spatial and cross-modal relationships to guide autonomous selection of next measurement location and experimental protocol Implemented on automated large-sample atomic force microscope; demonstrated on composition-spread AlScN wafer using tapping-mode and DART measurements Paired initial measurements establish task relationships; noncoincident measurements update response landscapes to extend active learning from spatial sampling to autonomous modality allocation
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a proof-of-concept demonstration of a new instrumental workflow on a single material system, without validation across materials or comparison to conventional approaches.
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Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials. Its increasing use for wafer-scale characterization and combinatorial materials exploration creates a need to distribute measurements efficiently across large spatial domains. This is particularly important when available modalities differ in acquisition time and potential for tip and sample damage, making exhaustive multimodal mapping over spatial grids impractical. Here, we demonstrate multitask scanning probe microscopy, a live, closed-loop workflow in which a multitask Gaussian process learns spatial and cross-modal relationships and autonomously selects both the next measurement location and the next experimental protocol. The approach is implemented on an automated large-sample atomic force microscope and demonstrated on a composition-spread AlScN wafer using tapping-mode and Dual AC Resonance Tracking (DART) measurements. Paired initial measurements establish the relation between the tasks, after which noncoincident measurements are used to update both response landscapes. The resulting workflow extends active learning in scanning probe microscopy from spatial sampling to autonomous allocation of measurement modalities and provides a basis for combining rapid, weakly perturbative imaging with slower contact, electrical, electromechanical, magnetic, or spectroscopic measurements.
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