Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

Effective molecular representation learning is of great importance to facilitate molecular
property prediction. Recent advances for molecular representation learning have shown …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

Many domains of science have developed complex simulations to describe phenomena of
interest. While these simulations provide high-fidelity models, they are poorly suited for …

Generating molecules that bind to specific proteins is an important but challenging task in
drug discovery. Most previous works typically generate atoms autoregressively, with element …

Hybrid quantum-classical variational algorithms are one of the most propitious
implementations of quantum computing on near-term devices, offering classical machine …

Recently, contrastive learning-based image translation methods have been proposed, which
contrasts different spatial locations to enhance the spatial correspondence. However, the …

We perform an analysis of the average generalization dynamics of large neural networks
trained using gradient descent. We study the practically-relevant “high-dimensional” regime …

ABSTRACT We present Galaxy Zoo DECaLS: detailed visual morphological classifications
for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint …