MaskTerial: A Foundation Model for Automated 2D Material Flake Detection

TL;DR

MaskTerial is a deep learning model that automates 2D material flake detection in microscopy images, improving accuracy especially for low-contrast materials and requiring minimal training data for new materials.

Contribution

The paper introduces MaskTerial, a novel instance segmentation model trained with synthetic data that adapts quickly to new 2D materials with minimal images.

Findings

Outperforms existing methods in detecting low-contrast materials

Requires only 5-10 images to adapt to new materials

Achieves significant accuracy improvements across multiple datasets

Abstract

The detection and classification of exfoliated two-dimensional (2D) material flakes from optical microscope images can be automated using computer vision algorithms. This has the potential to increase the accuracy and objectivity of classification and the efficiency of sample fabrication, and it allows for large-scale data collection. Existing algorithms often exhibit challenges in identifying low-contrast materials and typically require large amounts of training data. Here, we present a deep learning model, called MaskTerial, that uses an instance segmentation network to reliably identify 2D material flakes. The model is extensively pre-trained using a synthetic data generator, that generates realistic microscopy images from unlabeled data. This results in a model that can to quickly adapt to new materials with as little as 5 to 10 images. Furthermore, an uncertainty estimation model is used to finally classify the predictions based on optical contrast. We evaluate our method on eight different datasets comprising five different 2D materials and demonstrate significant improvements over existing techniques in the detection of low-contrast materials such as hexagonal boron nitride.