Since the advent of the first computers, chemists have been at the forefront of using
computers to understand and solve complex chemical problems. As the hardware and …

Electronic resonances are metastable states with finite lifetime embedded in the ionization
or detachment continuum. They are ubiquitous in chemistry, physics, and biology …

The power of quantum chemistry to predict the ground and excited state properties of
complex chemical systems has driven the development of computational quantum chemistry …

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the
naturally sparse connectivity information of the data. GNNs represent this connectivity as …

This Perspective presents a personal overview of the current status of the theory of chemical
kinetics and mechanisms for complex processes. We attempt to assess the status of the field …

Sparse tensors arise in problems in science, engineering, machine learning, and data
analytics. Programs that operate on such tensors can exploit sparsity to reduce storage …

We propose COSMA: a parallel matrix-matrix multiplication algorithm that is near
communication-optimal for all combinations of matrix dimensions, processor counts, and …

We present the NVIDIA cuQuantum SDK [1], a state-of-the-art library of composable
primitives for GPU-accelerated quantum circuit simulations. As the size of quantum devices …

This paper presents Tofu, a system that partitions very large DNN models across multiple
GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow …

This article presents an open‐source object‐oriented C++ library of classes and routines to
perform tensor algebra. The primary purpose of the library is to enable post‐Hartree–Fock …