Double-Hybrid, but Not Double-Cost: GPU-Accelerated DHDFT for the COMPAS-3 Data Set of Polybenzenoid Hydrocarbons

We present the first implementation of double hybrid density functional theory (DHDFT) with all major computational steps accelerated on GPUs. To efficiently utilize GPU hardware, we employ the resolution-of-identity (RI) approximation, transforming electron repulsion integral (ERI) computations into large dense matrix multiplications and enabling basis functions up to g angular momentum. We demonstrate revDSD-PBEP86-D4(noFC)/def2-QZVPP calculations on the entire COMPAS-3x data set of ∼39,000 peri-condensed polybenzenoid hydrocarbon isomer geometries (up to 68 atoms) using just 900 node-hours on the Perlmutter supercomputer. For medium-sized organic molecules (up to ∼3k basis functions), the PT2 component adds minimal cost relative to the initial SCF step. This demonstrates that efficient GPU acceleration reduces the practical computational requirements of DHDFT comparable to conventional hybrid DFT. We additionally benchmark a range of LDA, GGA, and MGGA functionals against the revDSD-PBEP86-D4(noFC) isomerization energies. Without dispersion corrections, the SVWN5 LDA functional (MAD 4.47 kJ/mol) outperforms all tested GGAs and MGGAs. With dispersion corrections, only two MGGAs, led by M06-L-D4 (MAD 3.82 kJ/mol), are able to surpass the SVWN5 results.