Abstract Machine learning is driving development across many fields in science and
engineering. A simple and efficient programming language could accelerate applications of …

Graph neural network (GNN)-based deep learning (DL) models have been widely
implemented to predict the experimental aqueous solvation free energy, while its prediction …

The prediction of quantum mechanical properties is historically plagued by a trade-off
between accuracy and speed. Machine learning potentials have previously shown great …

Combinatorial optimization problems are pervasive across science and industry. Modern
deep learning tools are poised to solve these problems at unprecedented scales, but a …

We propose GNNLab, a sample-based GNN training system in a single machine multi-GPU
setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to …

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as
their recursive numerical resolutions are often prohibitively expensive. It is mainly the case …

With recent advances in the Internet of Things (IoT) technology, more people can have
instant and easy access to the IoT network of vast and diverse interconnected devices (eg …

In the past few years, graph neural networks (GNNs) have become the de facto model of
choice for graph classification. While, from the theoretical viewpoint, most GNNs can operate …

Graph neural networks (GNNs) have shown high potential for a variety of real-world,
challenging applications, but one of the major obstacles in GNN research is the lack of large …

Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important
and challenging task for which quantitative structure–property relationship (QSPR) models …