Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

The generalization from empirically determined screened pseudopotentials to self-
consistently screened ab initio pseudopotentials had led to widespread use of the method in …

Valence electron-energy loss spectroscopy (VEELS) in a dedicated scanning transmission
electron microscope, vacuum ultraviolet spectroscopy and spectroscopic ellipsometry, and …

Graph neural networks (GNNs) have been used previously for identifying new crystalline
materials. However, geometric structure is not usually taken into consideration, or only …

A momentum-space formalism for calculating the total energy of solids is derived. This
formalism is designed particularly for application with the self-consistent pseudopotential …

We have used a self-consistent pseudopotential method to calculate the equilibrium ground-
state properties of the transition metals Mo and Nb. From our calculations we obtain …

This book presents a computational scheme for calculating the electronic properties of
crystalline systems at an ab-ini tio Hartree-Fock level of approximation. The first chapter is …

Cuprates exhibit antiferromagnetic, charge density wave (CDW), and high-temperature
superconducting ground states that can be tuned by means of doping and external magnetic …

Wide-band-gap semiconductors typically can be doped either n type or p type, but not both.
Compensation by native point defects has often been invoked as the source of this difficulty …

The description of realistic strongly correlated systems has recently advanced through the
combination of density functional theory in the local density approximation (LDA) and …