Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class
of machine learning models remarkably well-suited for materials applications. To date, a …

Graphs are flexible mathematical objects that can represent many entities and knowledge
from different domains, including in the life sciences. Graph neural networks (GNNs) are …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …

Graph neural networks (GNNs) constitute a class of deep learning methods for graph data.
They have wide applications in chemistry and biology, such as molecular property …

Graph neural networks (GNNs) have emerged as a powerful machine learning approach for
the prediction of molecular properties. In particular, recently proposed advanced GNN …

Deep learning is developing as an important technology to perform various tasks in
cheminformatics. In particular, graph convolutional neural networks (GCNs) have been …

Abstract Graph Neural Networks (GNNs) are a class of deep models that operates on data
with arbitrary topology and order-invariant structure represented as graphs. We introduce an …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Nowadays the development of new functional materials/chemical compounds using
machine learning (ML) techniques is a hot topic and includes several crucial steps, one of …