Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Electrostatic Discovery Atomic Force Microscopy   While offering unprecedented resolution of atomic and electronic structure,
Scanning Probe Microscopy techniques have found greater challenges in providing
reliable electrostatic characterization at the same scale. In this work, we
introduce Electrostatic Discovery Atomic Force Microscopy, a machine learning
based method which provides immediate quantitative maps of the electrostatic
potential directly from Atomic Force Microscopy images with functionalized
tips. We apply this to characterize the electrostatic properties of a variety
of molecular systems and compare directly to reference simulations,
demonstrating good agreement. This approach opens the door to reliable atomic
scale electrostatic maps on any system with minimal computational overhead.

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The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg