ABSTRACT
The phenomenon of polymorphism is ubiquitous in nature, the controlled manipulation of which not only increases our ontological understanding of nature but also facilitates the conceptualization and realization of novel functional materials. However, achieving targeted polymorphism in supramolecular assemblies (SAs) remains a formidable challenge, largely because of the constraints inherent in controlling the specific binding motifs of noncovalent interactions. Herein, we propose self-adaptive aromatic cation-π binding motifs to construct polymorphic SAs in both the solid and solution states. Using distinct discrete cation-π-cation and long-range cation-π binding motifs enables control of the self-assembly directionality of a C2h-symmetric bifunctional monomer, resulting in the successful formation of both two-dimensional and three-dimensional crystalline SAs (2D-CSA and 3D-CSA). The differences in the molecular packing of 3D-CSA compared with that of 2D-CSA significantly improve the charge separation and carrier mobility, leading to enhanced photocatalytic activity for the aerobic oxidation of thioanisole to methyl phenyl sulfoxide (yield of 99 % vs 57 %). 2D-CSA, which has a vertical extended structure with favorable stronger interaction with toluene though face-to-face cation-π interactions than methylcyclohexane, shows higher toluene/methylcyclohexane separation efficiency than 3D-CSA (96.9 % for 2D-CSA vs 56.3 % for 3D-CSA).
ABSTRACT
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/cm3), SeA provides a 1-2 order-of-magnitude speedup in the overall time-to-solution, i.e., ≈8-26× compared to the convolution-based PWSCF(ACE) implementation in QE and ≈78-247× 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 ≈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.