Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

The Random-Phase approximation (RPA) provides an appealing framework for semi-local density functional theory. In its Resolution-of-the-Identity (RI) approach it is a very accurate and more cost-effective method than most other wavefunction-based correlation methods. For widespread applications efficient implementations of nuclear gradients for structure optimizations and data sampling of machine learning approaches is required. We report a well scaling implementation of RI-RPA nuclear gradients on massively parallel computers. The approach is applied to two polymorphs of the benzene crystal obtaining very good cohesive and relative energies. Different correction and extrapolation schemes are investigated for further improvement of the results and estimations of error bars.