Extreme magnetoresistance in nonmagnetic compounds has received considerable attention because this phenomenon challenges the classical understanding of electron transport under a magnetic field.
Magnetic topological semimetals, a novel state of quantum matter with nontrivial band topology, have emerged as a new frontier in physics and materials science. An external stimulus like temperature or...
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 module
of (). The resulting approach ( = SCDM + + 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 (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
operator), and (iii) adaptively
compressed exchange (ACE, a low-rank
approximation). In doing so, harnesses three levels of computational savings: pair selection and domain truncation from
SCDM + (which only considers spatially
overlapping orbitals on orbital-pair-specific and system-size-independent
domains) and low-rank
approximation from ACE
(which reduces the number of calls to SCDM + 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/cm3), provides a 1−2 order-of-magnitude speedup
in the overall time-to-solution, i.e., ≈8−26× compared
to the convolution-based implementation
in and ≈78−247× compared
to the conventional 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 via an actively
learned data set with ≈8,700 (H2O)64 configurations.
Using an out-of-sample set of (H2O)512 configurations
(at nonambient conditions), we confirmed the accuracy of this -trained potential and showcased the capabilities
of by computing the ground-truth ionic
forces in this challenging system containing >1,500 atoms.
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