By admixing a fraction of exact exchange (EXX), hybrid functionals alleviate 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. This improvement is of central importance for accurately simulating the structural/Grotthuss transport of H₃O⁺ and OH^-, which are fundamental to acid-base chemistry. However, the high computational cost associated with hybrid DFT limits its applicability when treating such large-scale and complex condensed-phase systems. To address this challenge, we have constructed SeA (SeA=SCDM+exx+ACE)¹, a robust, accurate, and efficient framework for high-throughput condensed-phase hybrid DFT in the PWSCF module of Quantum ESPRESSO (QE) by combining: (1) the non-iterative selected columns of the density matrix (SCDM) orbital localization scheme, (2) a black-box and linear-scaling EXX algorithm (exx), and (3) adaptively compressed exchange (ACE). Across a diverse set of non-equilibrium (H₂O)₆₄ configurations (with densities spanning 0.4 g/cm³−1.7 g/cm³), SeA yields a one−two order-of-magnitude speedup (~8X−26X) in the overall time-to-solution compared to PWSCF(ACE) in QE (~78X−247X speedup compared to the conventional EXX implementation) and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ~8,700 (H₂O)₆₄ configurations. Using an out-of-sample set of (H₂O)₅₁₂ configurations (at non-ambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing > 1,500 atoms.

Zachary Sparrow

Appearances