  Developing theoretical understanding of complex reactions and processes at
interfaces requires using methods that go beyond semilocal density functional
theory to accurately describe the interactions between solvent, reactants and
substrates. Methods based on many-body perturbation theory, such as the random
phase approximation (RPA), have previously been limited due to their
computational complexity. However, this is now a surmountable barrier due to
the advances in computational power available, in particular through modern
GPU-based supercomputers. In this work, we describe the implementation of RPA
calculations within BerkeleyGW and show its favorable computational performance
on large complex systems relevant for catalysis and electrochemistry
applications. Our implementation builds off of the static subspace
approximation which, by employing a compressed representation of the frequency
dependent polarizability, enables the evaluation of the RPA correlation energy
with significant acceleration and systematically controllable accuracy. We find
that the computational cost of calculating the RPA correlation energy scales
only linearly with system size for systems containing up to 50 thousand bands,
and is expected to scale quadratically thereafter. We also show excellent
strong scaling results across several supercomputers, demonstrating the
performance and portability of this implementation.
