Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

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

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …

Research in chemistry increasingly requires interdisciplinary work prompted by, among
other things, advances in computing, machine learning, and artificial intelligence. Everyone …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Deep generative models of molecules have grown immensely in popularity, trained on
relevant datasets, these models are used to search through chemical space. The …

Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …