In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Conventional optical components such as lenses, waveplates and holograms rely on light
propagation over distances much larger than the wavelength to shape wavefronts. In this …

In recent years, the Transformer architecture has proven to be very successful in sequence
processing, but its application to other data structures, such as graphs, has remained limited …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Bone remodeling replaces old and damaged bone with new bone through a sequence of
cellular events occurring on the same surface without any change in bone shape. It was …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

Many aspects of the large-scale structure of the Universe can be described successfully
using cosmological models in which 27±1% of the critical mass-energy density consists of …

A core challenge for both physics and artificial intelligence (AI) is symbolic regression:
finding a symbolic expression that matches data from an unknown function. Although this …

The widespread use of molecular machines in biology has long suggested that great
rewards could come from bridging the gap between synthetic molecular systems and the …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …