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Machine-learning accelerated identification of exfoliable two-dimensional materials

Mohammad Tohidi Vahdat1,2, Kumar Agrawal Varoon2, Giovanni Pizzi1*

1 Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), EPFL, Lausanne, Switzerland

2 Laboratory of Advanced Separations (LAS), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

* Corresponding authors emails: giovanni.pizzi@epfl.ch
DOI10.24435/materialscloud:m4-7f [version v1]

Publication date: Jun 24, 2022

How to cite this record

Mohammad Tohidi Vahdat, Kumar Agrawal Varoon, Giovanni Pizzi, Machine-learning accelerated identification of exfoliable two-dimensional materials, Materials Cloud Archive 2022.85 (2022), doi: 10.24435/materialscloud:m4-7f.


Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

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File name Size Description
Machine-learning accelerated identification of exfoliable two-dimensional materials.zip
281.5 MiB This zip file contains all the necessary files to replicate the model
1.1 KiB Detail description of what you can find in the folder


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Paper in which the method is described)


Two-dimensional Exfoliation Crystal structure Binding energy Online tool

Version history:

2022.85 (version v1) [This version] Jun 24, 2022 DOI10.24435/materialscloud:m4-7f