Static Subspace Approximation for Random Phase Approximation Correlation Energies: Implementation and Performance

TL;DR

This paper presents an efficient implementation of RPA correlation energy calculations using static subspace approximation, enabling large-scale, accurate simulations for complex systems relevant to catalysis and electrochemistry.

Contribution

The authors introduce a novel implementation of RPA calculations with static subspace approximation that significantly improves computational efficiency and scalability on large systems.

Findings

Linear scaling of RPA correlation energy calculation up to 50,000 bands

Excellent strong scaling across multiple supercomputers

Systematic control of accuracy through compressed polarizability representation

Abstract

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.