Weinberg Daniel, Hull Olivia A, Clary Jacob M, Sundararaman Ravishankar, Vigil-Fowler Derek, Del Ben Mauro
Applied Mathematics & Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720-8099, United States.
Materials, Chemical, and Computational Science Directorate, National Renewable Energy Laboratory, Golden, Colorado 80401, United States.
J Chem Theory Comput. 2024 Sep 10. doi: 10.1021/acs.jctc.4c00807.
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.
要深入理解界面处复杂的反应和过程，需要采用超越半局域密度泛函理论的方法，以准确描述溶剂、反应物和底物之间的相互作用。此前，基于多体微扰理论的方法，如随机相位近似（RPA），因其计算复杂性而受到限制。然而，由于计算能力的提升，特别是通过基于现代GPU的超级计算机，这一障碍如今已可克服。在这项工作中，我们描述了在BerkeleyGW中实现RPA计算的过程，并展示了其在与催化和电化学应用相关的大型复杂系统上良好的计算性能。我们的实现基于静态子空间近似，通过采用频率相关极化率的压缩表示，能够显著加速RPA相关能的计算，并系统地控制精度。我们发现，对于包含多达5万个能带的系统，计算RPA相关能的计算成本仅与系统大小呈线性关系，此后预计呈二次方关系。我们还展示了在多个超级计算机上出色的强缩放结果，证明了该实现的性能和可移植性。
