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

Time-dependent density-functional theory (TDDFT) describes the quantum dynamics of
interacting electronic many-body systems formally exactly and in a practical and efficient …

Essentials of Computational Chemistry provides a balanced introduction to this dynamic
subject. Suitable for both experimentalists and theorists, a wide range of samples and …

It is well-known that time-dependent density functional theory (TDDFT) yields substantial
errors for the excitation energies of charge-transfer (CT) excited states, when approximate …

Electronic excitation energies are determined using the CAM-B3LYP Coulomb-attenuated
functional [T. Yanai et al Chem. Phys. Lett. 393, 51 (2004)], together with a standard …

The classic density-functional theory (DFT) formalism introduced by Hohenberg, Kohn, and
Sham in the mid-1960s is based on the idea that the complicated N-electron wave function …

Time-dependent density-functional theory (TDDFT) has become a well-established part of
the modern theoretical chemist's toolbox for treating electronic excited states. Yet, though …

There is a clear need for computationally inexpensive electronic structure theory methods
which can model excited state potential energy surfaces. Time-dependent density functional …

A quantitative understanding of molecular electronic excited states is important in many
domains, including spectroscopy, photochemistry, and the design of optical materials. The …

An approximate Kohn–Sham exchange-correlation potential ν xc SAOP is developed with
the method of statistical averaging of (model) orbital potentials (SAOP) and is applied to the …