The Grassmann manifold of linear subspaces is important for the mathematical modelling of
a multitude of applications, ranging from problems in machine learning, computer vision and …

The Hohenberg–Kohn theorem of density-functional theory (DFT) is broadly considered the
conceptual basis for a full characterization of an electronic system in its ground state by just …

We propose a scheme to construct predictive models for Hamiltonian matrices in atomic
orbital representation from ab initio data as a function of atomic and bond environments. The …

In this article, we present an overview of different a posteriori error analysis and post-
processing methods proposed in the context of nonlinear eigenvalue problems, eg arising in …

Density-functional theory (DFT) is a widespread method for simulating the quantum-
chemical behaviour of electrons in matter. It provides a first-principles description of many …

We have carried out quantum Monte Carlo (QMC) calculations of silicon crystal focusing on
the accuracy and systematic biases that affect the electronic structure characteristics. The …

This paper is devoted to the numerical solution of constrained energy minimization problems
arising in computational physics and chemistry such as the Gross–Pitaevskii and Kohn …

This work presents a general framework for deriving exact and approximate Newton self-
consistent field (SCF) orbital optimization algorithms by leveraging concepts borrowed from …

We introduce new correlation consistent effective core potentials (ccECPs) for the elements
I, Te, Bi, Ag, Au, Pd, Ir, Mo, and W with 4d, 5d, 6s, and 6p valence spaces. These ccECPs are …

This paper addresses the numerical solution of nonlinear eigenvector problems such as the
Gross–Pitaevskii and Kohn–Sham equation arising in computational physics and chemistry …