Predicting the chemico-physical properties of large molecular systems is a formidable challenge in chemistry and materials science. Traditional quantum mechanical methods, while accurate, have impractical scaling for large molecules with thousands of atoms, which are crucial in the development of novel therapeutics, catalysts, and nanomaterials. To address this, molecular fragmentation algorithms have been proposed to improve scalability and enable extensive parallelism. In this article, we introduce a significant enhancement to the Fragment Molecular Orbital (FMO) method, termed the Coulomb-Perturbed Fragmentation (CPF) method. CPF incorporates algorithmic improvements and implementation enhancements to optimize performance on heterogeneous computing systems equipped with a large number of GPUs. Key developments include a significant simplification of iteratitve self-consistent field (SCF) algorithm, advanced data management through a one-sided communication model, topology-aware optimizations, and a hybrid communication strategy for intra-group exchanges. Moreover, CPF integrates a distributed dynamic multi-layer load balancing scheme to optimise fragment distribution and workload management across nodes and GPUs. Performance evaluations on a 420-atom benzene molecule system comprising 35 fragments reveal that CPF outperforms existing GPU/CPU-based FMO algorithms in both efficiency and accuracy. When deployed on the Gadi supercomputer, CPF achieves over 97% parallel efficiency on 20 nodes, with scalability maintaining above 98% and 90% efficiency in weak-scaling tests for smaller and larger systems, respectively. Notably, CPF matches or exceeds the computational accuracy of conventional FMO methods, marking a substantial progress in the field of computational chemistry for fragmentation-based large-scale molecular modelling.

Correlated electronic structure calculations enable an accurate prediction of the physicochemical properties of complex molecular systems; however, the scale of these calculations is limited by their extremely high computational cost. The Fragment Molecular Orbital (FMO) method is arguably one of the most effective ways to lower this computational cost while retaining predictive accuracy. In this paper, a novel distributed many-GPU algorithm and implementation of the FMO method are presented. When applied in tandem with the Hartree-Fock and RI-MP2 methods, the new implementation enables correlated calculations on 623,016 electrons and 146,592 atoms in less than 45 minutes using 99.8% of the Summit supercomputer (27,600 GPUs). The implementation demonstrates remarkable speedups with respect to other current GPU and CPU codes, and excellent strong scalability on Summit achieving 94.6 % parallel efficiency on 4600 nodes. This work makes feasible correlated quantum chemistry calculations on significantly larger molecular systems than before and with higher accuracy.