Ab initio nonadiabatic dynamics of multichromophore complexes: a scalable graphical-processing-unit-accelerated...

Conspectus Although advances in computer hardware and algorithms tuned for novel computer architectures are leading to significant increases in the size and time scale for molecular simulations, it remains true that new methods and algorithms will be needed to address some of the problems in complex chemical systems, such as electrochemistry, excitation energy transport, proton transport, and condensed phase reactivity. Ideally, these new methods would exploit the strengths of emerging architectures. Fragment based approaches for electronic structure theory decompose the problem of solving the electronic Schrodinger equation into a series of much smaller problems. Because each of these smaller problems is largely independent, this strategy is particularly well-suited to parallel architectures. It appears that the most significant advances in computer architectures will be toward increased parallelism, and therefore fragment-based approaches are an ideal match to these trends. When the computational effort involved scales with the third (or higher) power of the molecular size, there is a large benefit to fragment-based approaches even on serial architectures. This is the case for many of the well-known methods for solving the electronic structure theory problem, especially when wave function-based approaches including electron correlation are considered. A major issue in fragment-based approaches is determining or improving their accuracy. Since the Achilles' heel of any such method lies in the approximations used to stitch the smaller problems back together (i.e., in the treatment of the cross-fragment interactions), it can often be important to ensure that the size of the smaller problems is "large enough." Thus, there are two frontiers that need to be extended in order to enable molecular simulations for large systems and long times: the strongly coupled problem of medium sized molecules (100-500 atoms) and the more weakly coupled problem of decomposing ("fragmenting") a molecular system and then stitching it back together. In this Account, we address both of these problems, the first by using graphical processing units (GPUs) and electronic structure algorithms tuned for these architectures and the second by using an exciton model as a framework in which to stitch together the solutions of the smaller problems. The multitiered parallel framework outlined here is aimed at nonadiabatic dynamics simulations on large supramolecular multichromophoric complexes in full atomistic detail. In this framework, the lowest tier of parallelism involves GPU-accelerated electronic structure theory calculations, for which we summarize recent progress in parallelizing the computation and use of electron repulsion integrals (ERIs), which are the major computational bottleneck in both density functional theory (DFT) and time-dependent density functional theory (TDDFT). The topmost tier of parallelism relies on a distributed memory framework, in which we build an exciton model that couples chromophoric units. Combining these multiple levels of parallelism allows access to ground and excited state dynamics for large multichromophoric assemblies. The parallel excitonic framework is in good agreement with much more computationally demanding TDDFT calculations of the full assembly.