  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 a weak binding energy that, 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.
