Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry
and condensed matter physics. Efficiency and equivariance are two important, but conflicting …

Proteins are complex biomolecules that perform a variety of crucial functions within living
organisms. Designing and generating novel proteins can pave the way for many future …

Graph neural networks are emerging as promising methods for modeling molecular graphs,
in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent …

Crystal structures are characterized by atomic bases within a primitive unit cell that repeats
along a regular lattice throughout 3D space. The periodic and infinite nature of crystals …

Physical simulations of fluids are crucial for understanding fluid dynamics across many
applications, such as weather prediction and engineering design. While high-resolution …

Predicting quantum chemical properties is a fundamental challenge for computational
chemistry. While the development of graph neural networks has advanced molecular …

Ground-state 3D geometries of molecules are essential for many molecular analysis tasks.
Modern quantum mechanical methods can compute accurate 3D geometries but are …

A molecule's 2D representation consists of its atoms, their attributes, and the molecule's
covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and …