Fueling a Data-Driven Machine Learning Model for H₃O⁺ and OH^- Transport through Confined Aqueous Environments: A High-Throughput Order-N Framework for Condensed-Phase Hybrid Density Functional Theory at Work

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local density functional theory (DFT), and thereby furnish a more accurate and reliable description of the electronic structure in systems throughout chemistry, physics, and materials science. In particular, it has been demonstrated that dispersion-inclusive hybrid DFT can provide a semi-quantitative description of H₃O⁺ and OH^- transport in bulk aqueous solutions. However, the high computational cost associated with hybrid DFT limits its applicability when treating such large-scale and complex condensed-phase systems. To overcome this limitation, we have developed a highly accurate linear-scaling (order-N) approach for treating finite-gap (homogeneous and heterogeneous) systems without system-dependent parameters. Furthermore, we have implemented and devised a GPU-accelerated implementation of this framework to generate high-quality dispersion-inclusive hybrid DFT data for building a deep neural network potential for aqueous H₃O⁺ and OH^- in bulk and confined environments. With these developments, this work brings us closer to understanding H₃O⁺ and OH^- transport through confined aqueous environments, which is of fundamental importance in the energy sciences.