Graph neural networks (GNNs) have emerged as a powerful ... information from neighboring nodes and edges using a message-passing scheme. This allows GNNs to capture local and global patterns ...

In an effort to overcome these limitations in accuracy, more recently, deep learning interatomic potentials based on the message passing neural network (MPNN) paradigm have been proposed and shown to ...

The research systematically investigates whether UQ-enhanced directed message passing neural networks (D-MPNNs) can effectively guide optimization across broad, open-ended chemical spaces ...

For instance, Yale University and IBM recently proposed a concept they call Kernel Graph Neural Networks (KerGNNs), which integrate graph kernels into GNN message-passing. This is the process by ...

At the heart of this work is what the Google Brain team calls Message Passing Neural Networks, which takes traditional approaches to quantum chemistry, refits them into neural networks, and shows ...

Graph Neural Networks are on the path to becoming more mainstream, with exciting opportunities for the maturation of essential operations across message passing such as scatters, gathers, segmented ...

Chemprop is what's known as a "directed-message passing neural network," or D-MPNN. The message-passing phenomenon is now well-established in the deep learning literature as a way to handle ...

A directed message-passing neural network (D-MPNN) was utilized to estimate activation energies (AEs) for pyrolysis reactions, focusing on those producing high-risk health products. The derived AE ...