Stress-based structure optimization for a range-separated hybrid van der Waals density functional
Complex matter, often partly soft and pliable, is the generic form of material systems. We must therefore generally call on density functional theory (DFT) to first predict the atomic structure before we can use it to also characterize (expected) properties. A recent range-separated hybrid (RSH) van Waals density functional (vdW-DF), `vdW-DF2-ahbr' (abbreviated AHBR) [PRX 12, 041003 (2022)], shows promise as a high-accuracy predictor of both binding energies and structure, from molecules to solids. However, the present implementation in Quantum Espresso (QE) does not, in our experience, support robust stress-based unit-cell optimization of RSHs. Here we document and illustrate work enabling practical AHBR-based complex-matter discovery: We port the AHBR XC functional to the Vienna Ab Initio Simulation Package (VASP) where use of stress-based DFT optimization is already stable also for RSHs. We test the implementation by comparing high-accuracy AHBR-QE and AHBR-VASP predictions of non-covalent molecular interactions and for structure and cohesion of simple bulk structures. We also illustrate and test the use of the new AHBR-VASP implementation to predict and understand (atomic and anti-ferromagnetic) structure in distorted-rocksalt metal monoxides. Finally, we illustrate use for complex-soft-matter discovery by predicting motifs for layer stacking in the C2N covalent-organic framework (COF) system [Nat. Commun. 6, 6486 (2015)].