PySCF-NAO: An efficient and flexible implementation of linear response time-dependent density functional theory with numerical atomic orbitals
Description
We present an algorithm and its implementation to calculate the properties of electronic excitations in molecules and clusters from first principles, using time-dependent density functional theory (TDDFT). The algorithm assumes the use of some localized functions as a basis set to represent the spatial degrees of freedom. It relies on an iterative computation of the induced density according to the Dyson-like equation for the linear response function. The current implementation is built upon so-called numerical atomic orbitals. It is suitable for a wide variety of density functional theory (DFT) software. In this work, we demonstrate TDDFT calculations starting from preceding DFT runs with SIESTA, GPAW and PySCF packages, while a coupling with the other DFT packages such as Fireball and OpenMX is planned. The mentioned packages are capable of performing ab initio molecular dynamics simulations, and the speed of our TDDFT implementation makes feasible to perform a configuration average of the optical absorption spectra. Our code is written mostly in Python language allowing for a quick and compact implementation of most numerical methods and data-managing tasks with the help of NumPy/SciPy libraries and Python intrinsic constructs. Part of the code is written in C and Fortran to achieve a competitive speed in particular sections of the algorithm. Many parts of the current algorithm and implementation are useful in other ab initio methods for electronic excited states, such as Hedin’s GW, Bethe–Salpeter equation and DFT with hybrid functionals. Corresponding proof-of-principles implementations are already part of the code.