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Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM

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arxiv 2501.02503 v2 pith:UK7FUES4 submitted 2025-01-05 cond-mat.mtrl-sci physics.app-ph

classification cond-mat.mtrl-sciphysics.app-ph
keywords synthesisapplicationsmaterialmaterialsmicroscopybeencombinatorialdata
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For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Closed-Loop Optimization to Open Decision Making: Coupled Digital Twins for Predictive and Autonomous Microscopy

    cond-mat.mtrl-sci 2026-07 conditional novelty 6.5 of 10

    Coupled physics-informed sample and instrument digital twins recover AM-SPM force-distance descriptors to ~0.4 nm and predict scan quality and safety after sparse residual correction.

  2. Advancing from Automated to Autonomous Beamline by Leveraging Computer Vision

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A multi-camera computer vision system using segmentation, tracking, and pixel-distance checks detects beamline collisions in real time, reporting 99.8% accuracy on a small in-house dataset.

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