This project implements a Graph Variational Autoencoder (VAE) for molecule generation, inspired by the paper Graph Variational Autoencoders for Molecule Generation. The implementation leverages the ...

In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has ...

The graph autoencoder adds the graph attention mechanism ... The structure of the GAE is shown in Figure 2B, which takes the trimmed cell diagram as input. The encoder is composed of two GAT ...

In this paper, we propose a marginalized graph autoencoder with subspace structure preserving, which adds a self-expressive layer to reveal the clustering structure of node attributes based on the ...

A PyTorch implementation of a Graph Autoencoder (GAE) for Reduced Order Modeling (ROM) of Navier-Stokes equations on unstructured meshes. This project provides an efficient framework for learning ...

In this work, we utilized autoencoder-based graph clustering to analyze discontinuous molecular dynamics (DMD) simulations of lysozyme adsorption on a graphene surface. Our high-throughput DMD ...