@misc{antonDatasetMethodologyMaterial2025,
	title = {Dataset and Methodology for Material Identification Using {AFM} Phase Approach Curves},
	url = {http://arxiv.org/abs/2504.01636},
	doi = {10.48550/arXiv.2504.01636},
	abstract = {Atomic force microscopy ({AFM}) phase approach-curves have significant potential for nanoscale material characterization, however, the availability of robust datasets and automated analysis tools has been limited. In this paper, we introduce a novel methodology for material identification using a high-dimensional dataset consisting of {AFM} phase approach-curves collected from five distinct materials: silicon, silicon dioxide, platinum, silver, and gold. Each measurement comprises 50 phase values obtained at progressively increasing tip-sample distances, resulting in 50x50x50 voxel images that represent phase variations at different depths. Using this dataset, we compare k-nearest neighbors ({KNN}), random forest ({RF}), and feedforward neural network ({FNN}) methods for material segmentation. Our results indicate that the {FNN} provides the highest accuracy and F1 score, outperforming more traditional approaches. Finally, we demonstrate the practical value of these segmented maps by generating simulated scattering-type scanning near-field optical microscopy (s-{SNOM}) images, highlighting how {AFM} phase approach-curves can be leveraged to produce detailed, predictive tools for nanoscale optical analysis.},
	number = {{arXiv}:2504.01636},
	publisher = {{arXiv}},
	author = {Anton, Stefan R. and Tranca, Denis E. and Stanciu, Stefan G. and Ionescu, Adrian M. and Stanciu, George A.},
	urldate = {2026-06-17},
	date = {2025-04-05},
	eprinttype = {arxiv},
	eprint = {2504.01636 [physics.optics]},
	keywords = {Physics - Optics, preprint},
}