Simple approach to more efficient density functional theory simulations

In physics, chemistry and materials science, density functional theory (DFT) is a common method for studying the electronic structure of many-body systems. DFT can address complex materials with relatively low computational costs compared to other physics-based methods; however, it still requires considerable computational power for large-scale simulations. With the aim of reducing the computational footprint of DFT, we suggest the use of the data-efficient Bayesian algorithm to optimize the charge mixing parameters, which reduces the self-consistent field iterations necessary to reach convergence. We show that our algorithm can achieve faster convergence than the default parameters when the VASP code is used as a proof of concept, resulting in significant time savings in DFT simulations and therefore providing a systematic guide for more efficient DFT simulations. We propose adding this procedure to the well-known convergence test procedures, such as cutoff-energy and k-point convergence tests.