Benchmark comparison of dual-basis double-hybrid density functional theory and a neural-network-optimized method for intermolecular interactions

We present a computationally efficient implementation of double-hybrid density functional theory (DH-DFT) leveraging the dual basis methods Head-Gordon and co-workers resolution-of-the-identity second-order Møller-Plesset (RI-MP2) theory. The B2PLYP, B2GP-PLYP, DSD-BLYP DSD-PBEP86 functionals are applied to assess performance dual-basis on several benchmark test cases, including CONF set conformational energy differences in C4-C7 alkanes, S22 noncovalent interaction energies, RGC10 noble-gas dimer dissociation curves. DH-DFT approach is shown give results excellent agreement with conventional at reduced computational cost. For compared against leading neural-network-based approach, namely SNS-MP2 method McGibbon coworkers (McGibbon et al., 2017). produce similar accuracies when established values.