Rapid Generation of Optimal Generalized Monkhorst-Pack Grids

The calculation of properties of crystalline materials can often be accelerated by using k-point grids to estimate an integral in reciprocal space. We have shown that a generalization of the widely-used Monkhorst-Pack method for k-point grid generation can reduce the number of symmetrically distinct k-points required to reach a given level of accuracy, and thus accelerate computational throughput, by roughly a factor of two for well-converged density functional theory calculations. To facilitate the widespread adoption of this approach, we have developed algorithms that enable dynamic generation of optimal generalized Monkhorst-Pack grids within seconds. We present these algorithms and several software tools in which they are implemented, including a C++ library with a Python interface designed for integration with third-party software packages.