Hybrid DFT Quality Thermochemistry and Environment Effects at GGA Cost via Local Quantum Embedding

Reliable thermochemical
modeling of reaction mechanisms requires
hybrid DFT or higher-level models as well as inclusion of environment,
conformer, thermal, etc. effects. Quantum embedding, such as the Huzinaga-equation
and projection-based models employed here, can make such computations
more accessible by focusing the use of the more costly models to the
atoms involved in forming and breaking the bonds or residing in interacting
surfaces, etc. Here, we further accelerate these embedding computations
by combining local approximations in the atomic orbital and auxiliary
function space of the hybrid DFT part with a new in-core density fitting
implementation optimized for multilayer DFT. The so introduced local
embedded subsystem (LESS) framework, when increasing the size of the
environment, leads to asymptotically constant cost for the hybrid
DFT layer. We demonstrate on reaction and activation energies of practical
homogeneous, heterogeneous and enzymatic catalysis reactions that
the intrinsic accuracy of hybrid DFT is retained, with a few tenths
of a kcal/mol error including all (embedding and local) approximations.
Compared to the same complete (density fitted) hybrid DFT reference,
the LESS hybrid DFT-in-GGA runtimes are 30–90 times faster
on systems with up to 171–238 atoms. Achieving energetics with
practically hybrid DFT quality and GGA cost is a significant step
toward predictive thermochemistry including reliable sampling, dynamics,
etc. as well as quantum environment effects.