# High-Throughput Evaluation of Mechanical Exfoliation Using Optical Classification of Two-Dimensional Materials

## TL;DR

This paper introduces a fast, open-source software for analyzing 2D materials using machine learning, significantly speeding up the evaluation of mechanical exfoliation processes.

## Contribution

The novel contribution is a GPU-accelerated platform that enables high-throughput analysis of 2D material samples with minimal manual input.

## Key findings

- The software processes over 200× more pixel data with a 60× reduction in processing time compared to existing tools.
- A dataset of 2916 images is classified in 35 minutes, versus an estimated 32 hours with the baseline method.
- The platform enables rapid evaluation of exfoliation results, improving the yield of high-quality 2D materials.

## Abstract

Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials.