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An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
Mesoscale and Nanoscale Physics · Submitted 26 Jun 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work.
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The abstract, sentence by sentence
The most widely used method for obtaining high-quality two-dimensional materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. Here, we present a platform for fully automated scanning, detection, and classification of two-dimensional materials, the source code of which we make openly available. Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work. The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2.3 Mpixels. The developed detection algorithm is based on a combination of the flakes' optical contrast toward the substrate and their geometric shape. We demonstrate that it is able to detect the majority of exfoliated flakes of various materials, with an average recall (AR50) between 67% and 89%. We also show that the algorithm can be trained with as few as five flakes of a given material, which we demonstrate for the examples of few-layer graphene, WSe$_2$, MoSe$_2$, CrI$_3$, 1T-TaS$_2$ and hexagonal BN. Our platform has been tested over a two-year period, during which more than $10^6$ images of multiple different materials were acquired by over 30 individual researchers.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The most widely used method for obtaining high-quality two-dimensional materials is through mechanical exfoliation of bulk crystals. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. | background 0.84 | 0.09 | 0.02 | asserted 1.00 |
| s2 | Here, we present a platform for fully automated scanning, detection, and classification of two-dimensional materials, the source code of which we make openly available. | contribution 1.00 | 0.94 | 0.35 | asserted 1.00 |
| s3 | Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work. | contribution 0.80 | 0.50 | 0.16 | asserted 0.99 |
| s4 | The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2.3 Mpixels. | result 0.62 | 0.93 | 0.09 | asserted 1.00 |
| s5 | The developed detection algorithm is based on a combination of the flakes' optical contrast toward the substrate and their geometric shape. | method 1.00 | 0.82 | 0.06 | asserted 1.00 |
| s6 | We demonstrate that it is able to detect the majority of exfoliated flakes of various materials, with an average recall (AR50) between 67% and 89%. | result 1.00 | 0.97 | 0.20 | asserted 1.00 |
| s7 | We also show that the algorithm can be trained with as few as five flakes of a given material, which we demonstrate for the examples of few-layer graphene, WSe$_2$, MoSe$_2$, CrI$_3$, 1T-TaS$_2$ and hexagonal BN. | result 0.95 | 0.97 | 0.11 | asserted 1.00 |
| s8 | Our platform has been tested over a two-year period, during which more than $10^6$ images of multiple different materials were acquired by over 30 individual researchers. | method 0.51 | 0.93 | 0.07 | asserted 1.00 |
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Original abstract
The most widely used method for obtaining high-quality two-dimensional materials is through mechanical exfoliation of bulk crystals. Manual identification of suitable flakes from the resulting random distribution of crystal thicknesses and sizes on a substrate is a time-consuming, tedious task. Here, we present a platform for fully automated scanning, detection, and classification of two-dimensional materials, the source code of which we make openly available. Our platform is designed to be accurate, reliable, fast, and versatile in integrating new materials, making it suitable for everyday laboratory work. The implementation allows fully automated scanning and analysis of wafers with an average inference time of 100 ms for images of 2.3 Mpixels. The developed detection algorithm is based on a combination of the flakes' optical contrast toward the substrate and their geometric shape. We demonstrate that it is able to detect the majority of exfoliated flakes of various materials, with an average recall (AR50) between 67% and 89%. We also show that the algorithm can be trained with as few as five flakes of a given material, which we demonstrate for the examples of few-layer graphene, WSe$_2$, MoSe$_2$, CrI$_3$, 1T-TaS$_2$ and hexagonal BN. Our platform has been tested over a two-year period, during which more than $10^6$ images of multiple different materials were acquired by over 30 individual researchers.
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The (pixel-wise determined) recall has been shown to be as high or surpassing those reported for implementations using neural networks [ref] .
Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images · Discussion · 1.00
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