How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing
Not on the map. Why?
Abstract (arXiv)
We focus on the potential possibilities for supporting Scanning Probe Microscopy measurements, emphasizing the application of Artificial Intelligence, especially Machine Learning as well as quantum computing. It turned out that Artificial Intelligence can be helpful in the experimental processes automation in routine operations, the algorithmic search for good sample regions, and shed light on the structure property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of Artificial Intelligence based algorithms and quantum computing may have a huge potential to increase the practical application of Scanning Probe Microscopy. The limitations were also discussed. Finally, we outline a research path for the improvement of the proposed approach.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 258 references that are in the map. First posted on arXiv 20 Mar 2024.
Builds on
11 of 258 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
- 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 278
- 2017Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networkscited by 138
- 2021Theory of overparametrization in quantum neural networkscited by 100
- 2020Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Networkcited by 38
- 2021Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentationcited by 34
- 2018A Generative Model for Inverse Design of Metamaterialscited by 27
- 2022Analytic theory for the dynamics of wide quantum neural networkscited by 21
- 2019Scanning Probe State Recognition With Multi-Class Neural Network Ensemblescited by 2
- 2022AtomVision: A machine vision library for atomistic imagescited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.