Multitask Scanning Probe Microscopy

• URL: http://arxiv.org/abs/2608.09104v1
• Date: Mon, 10 Aug 2026 04:19:16 GMT
• Title: Multitask Scanning Probe Microscopy
• Abstract summary: multitask scanning probe microscopy is a live, closed-loop workflow.<n>Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials.
• Score: 3.126650763625235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PATHFINDER: Multi-objective discovery in structural and spectral spaces [0.6738747756464843]
  PATHFINDER is a framework for autonomous microscopy that combines novelty driven exploration with optimization.<n>It selects measurements that balance novelty discovery in feature and object space and are informative and experimentally actionable.<n>These results point to a new mode of autonomous microscopy that is not only optimization-driven, but also discovery-oriented, broad, and responsive to human guidance.
  arXiv  Detail & Related papers  (2026-04-05T17:31:08Z)
• Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs [0.837047113864403]
  Combinatorial materials libraries are an efficient route to generate large families of candidate compositions.<n>We develop an autonomous scanning probe microscopy framework that integrates automated atomic force and magnetic force microscopy.
  arXiv  Detail & Related papers  (2026-01-09T04:55:36Z)
• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation [48.267001230607306]
  We propose a pretraining framework that enhances multiscale consistency in EM volumes. Our approach leverages a Siamese network architecture, integrating strong and weak data augmentations. It effectively captures voxel and feature consistency, showing promise for learning transferable representations for EM analysis.
  arXiv  Detail & Related papers  (2023-08-19T05:49:13Z)
• Machine learning-based spin structure detection [0.0]
  We report a convolutional neural network specifically designed for segmentation problems to detect the position and shape of skyrmions in our measurements. The results of this study show that a well-trained network is a viable method of automating data pre-processing in magnetic microscopy.
  arXiv  Detail & Related papers  (2023-03-24T17:19:31Z)
• Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images [2.5954872177280346]
  Machine learning could revolutionize materials research through autonomous data collection and processing. We employ a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator to augment simulated data with realistic spatial frequency information. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set.
  arXiv  Detail & Related papers  (2023-01-18T19:19:27Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• Toward deep-learning-assisted spectrally-resolved imaging of magnetic noise [52.77024349608834]
  We implement a deep neural network to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field. These results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.
  arXiv  Detail & Related papers  (2022-08-01T19:18:26Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns [48.53244254413104]
  We implement a deep neural network called FCU-Net to invert highly nonlinear electron diffraction patterns into quantitative structure factor images. We trained the FCU-Net using over 200,000 unique dynamical diffraction patterns which include many different combinations of crystal structures. Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories.
  arXiv  Detail & Related papers  (2022-02-01T03:53:39Z)
• High resolution functional imaging through Lorentz transmission electron microscopy and differentiable programming [4.717645818081808]
  Lorentz transmission electron microscopy is a unique characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. It is necessary to retrieve the complete wavefunction of the electron wave, which requires solving for the phase shift of the electrons. Here we have developed a method based on differentiable programming to solve the inverse problem of phase retrieval.
  arXiv  Detail & Related papers  (2020-12-07T20:26:53Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.