Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

Self-supervised learning (SSL) has been extensively explored in recent years. Particularly,
generative SSL has seen emerging success in natural language processing and other …

Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

This work introduces a diffusion model for molecule generation in 3D that is equivariant to
Euclidean transformations. Our E (3) Equivariant Diffusion Model (EDM) learns to denoise a …

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

Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …