DFT Embedding in Python for Realistically-sized Systems

With modern hardware advances, the exascale computing era has begun. Thus, for computational chemistry and physics, multiscale models of materials and biological systems are arguably the most important platforms for tackling the challenges ahead. Nowadays, density functional theory (DFT) is the method of choice. However, DFT's computational scaling hampers its applicability to realistically sized systems. In an effort to retain the predictivity of DFT while at the same time reducing the computational cost, we present eDFTpy, a density embedding Python code. In eDFTpy, the system is split into weakly interacting subsystems treated either at the Kohn-Sham DFT level or at the orbital-free DFT level. Inter-subsystem interactions are evaluated with orbital-free DFT. This leads to a linear-scaling, low prefactor algorithm having essentially KS-DFT accuracy that can be used for realistically-sized systems. The parallelization scheme of eDFTpy is state-of-the-art: it has low memory cost and small communication time, thus strong parallel scaling to thousands of cores is achieved.