High-Throughput Condensed-Phase Hybrid Density Functional Theory for
Large-Scale Finite-Gap Systems: The
HY Ko and MFC Andrade and ZM Sparrow and JA Zhang and RA Jr Distasio, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 19, 4182-4201 (2023).
DOI: 10.1021/acs.jctc.2c00827
High-throughput electronic structure calculations (often performed using density functional theory (DFT)) play a central role in screening existing and novel materials, sampling potential energy surfaces, and generating data for machine learning applications. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self- interaction error in semilocal DFT and furnish a more accurate description of the underlying electronic structure, albeit at a computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed a robust, accurate, and computationally efficient framework for high-throughput condensed-phase hybrid DFT and implemented this approach in the PWSCF module of Quantum ESPRESSO (QE). The resulting SeA approach (SeA = SCDM + exx + ACE) combines and seamlessly integrates: (i) the selected columns of the density matrix method (SCDM, a robust noniterative orbital localization scheme that sidesteps system-dependent optimization protocols), (ii) a recently extended version of exx (a black-box linear- scaling EXX algorithm that exploits sparsity between localized orbitals in real space when evaluating the action of the standard/full-rank V-xx operator), and (iii) adaptively compressed exchange (ACE, a low-rank V-xx approximation). In doing so, SeA harnesses three levels of computational savings: pair selection and domain truncation from SCDM + exx (which only considers spatially overlapping orbitals on orbital- pair-specific and system-size-independent domains) and low-rank V-xx approximation from ACE (which reduces the number of calls to SCDM + exx during the self-consistent field (SCF) procedure). Across a diverse set of 200 nonequilibrium (H2O)(64) configurations (with densities spanning 0.4-1.7 g/cm(3)), SeA provides a 1-2 order-of-magnitude speedup in the overall time-to-solution, i.e., approximate to 8-26x compared to the convolution-based PWSCF(ACE) implementation in QE and approximate to 78-247x compared to the conventional PWSCF(Full) approach, 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 approximate to 8,700 (H2O)(64) configurations. Using an out-of-sample set of (H2O)(512) configurations (at nonambient 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.
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