Enhanced Atomic Force Microscopy Data Analysis via Hyperdimensional Vector Representation and Recurrent Neural Network Autoencoders

**Abstract:** This paper introduces a novel approach to Atomic Force Microscopy (AFM) data analysis, leveraging hyperdimensional vector representations and recurrent neural network (RNN) autoencoders to enhance feature extraction and noise reduction. Traditional AFM data analysis relies on statistical methods and manual feature engineering, which can be time-consuming and prone to subjective bias. Our method converts AFM height maps into high-dimensional hypervectors, enabling the capture of subtle spatial relationships and patterns. These hypervectors are then processed by RNN autoencoders to learn compressed representations of the data, effectively filtering noise and highlighting salient structural features. The resulting feature vectors can be used for automated material classification, defect detection, and surface roughness quantification, offering a significant improvement in efficiency and accuracy compared to existing methods. This system is immediately actionable given existing HPC and current AFM technology, projecting a deployment timeframe of 1-3 years for advanced material characterization.