The coupled-cluster (CC) singles and doubles with perturbative triples [CCSD(T)] method is frequently referred to as the “gold standard” of modern computational chemistry. However, the high computational cost of CCSD(T) [ O ( N 7 )], where N is the number of basis functions, limits its applications to small-sized chemical systems. To address this problem, efficient implementations of linear-scaling coupled-cluster methods, which employ the systematic molecular fragmentation (SMF) approach, are reported. In this study, we aim to do the following: (1) To achieve exact linear scaling and to obtain a pure ab initio approach, we revise the handling of nonbonded interactions in the SMF approach, denoted by LSSMF. (2) A new fragmentation algorithm, which yields smaller-sized fragments, that better fits high-level CC methods is introduced. (3) A modified nonbonded fragmentation scheme is proposed to enhance the existent algorithm. Performances of the LSSMF-CC approaches, such as LSSMF-CCSD(T), are compared with their canonical versions for a set of alkane molecules, C n H 2 n +2 ( n = 6–10), which includes 142 molecules. Our results demonstrate that the LSSMF approach introduces negligible errors compared with the canonical methods; mean absolute errors (MAEs) are between 0.20 and 0.59 kcal mol –1 for LSSMF(3,1)-CCSD(T). For a larger alkanes set (L12), C n H 2 n +2 ( n = 50–70), the performance of LSSMF for the second-order perturbation theory (MP2) is investigated. For the L12 set, various bonded and nonbonded levels are considered. Our results demonstrate that the combination of bonded level 6 with nonbonded level 2, LSSMF(6,2), provides very accurate results for the MP2 method with a MAE value of 0.32 kcal mol –1 . The LSSMF(6,2) approach yields more than a 26-fold reduction in errors compared with LSSMF(3,1). Hence, we obtain substantial improvements over the original SMF approach. To illustrate the efficiency and applicability of the LSSMF-CCSD(T) approach, we consider an alkane molecule with 10,004 atoms. For this molecule, the LSSMF(3,1)-CCSD(T)/cc-pVTZ energy computation, on a Linux cluster with 100 nodes, 4 cores, and 5 GB of memory provided to each node, is performed just in ∼24 h. As a second test, we consider a biomolecular complex (PDB code: 1GLA ), which includes 10,488 atoms, to assess the efficiency of the LSSMF approach. The LSSMF(3,1)-FNO–CCSD(T)/cc-pVTZ energy computation is completed in ∼7 days for the biomolecular complex. Hence, our results demonstrate that the LSSMF-CC approaches are very efficient. Overall, we conclude the following: (1) The LSSMF( m , n )-CCSD(T) methods can ...

MacroQC is a quantum chemistry software for high-accuracy computations and large-scale chemical applications. MacroQC package features energy and analytic gradients for a broad range of many-body perturbation theory and coupled-cluster (CC) methods. Even when compared to commercial quantum chemistry software, analytical gradients of second-order perturbation theory, CC singles and doubles (CCSD), and CCSD with perturbative triples approaches are particularly efficient. MacroQC has a number of peculiar features, such as analytic gradients with the density-fitting approach, orbital-optimized methods, extended Koopman’s theorem, and molecular fragmentation approaches. MacroQC provides a limited level of interoperability with some other software. The plugin system of MacroQC allows external interfaces in a developer-friendly way. The linear-scaling systematic molecular fragmentation (LSSMF) method is another distinctive feature of the MacroQC software. The LSSMF method enables one to apply high-level post-Hartree–Fock methods to large-sized molecular systems. Overall, we feel that the MacroQC program will be a valuable tool for wide scientific applications.

Accurate computation of electron affinities (EAs), within 0.10 eV, is one of the most challenging problems in modern computational quantum chemistry. The extended Koopmans' theorem (EKT) enables direct computations of electron affinities (EAs) from any level of the theory. In this research, the EKT approach based on the coupled-cluster singles and doubles with perturbative triples [CCSD(T)] method is applied to computations of EAs for the first time. For efficiency, the density-fitting (DF) technique is used for two-electron integrals. Further, the EKT-CCSD(T) method is applied to three test sets of atoms and closed-and open-shell molecules, denoted A16, C10, and O33, respectively, for comparison with the experimental electron affinities. For the A16, C10, and O33 sets, the EKT-CCSD(T) approach, along with the augcc-pV5Z basis set, provide mean absolute errors (MAEs) of 0.05, 0.08, and 0.09 eV, respectively. Hence, our results demonstrate that high-accuracy computations of EAs can be achieved with the EKT-CCSD(T) approach. Further, when the EKT-CCSD(T) approach is not computationally affordable, the EKT-MP2.5, EKT-LCCD, and EKT-CCSD methods can be considered, and their results are also reasonably accurate. The huge advantage of the EKT method for the computation of IPs is that it comes for free in an analytic gradient computation. Hence, one needs neither separate computations for neutral and ionic species, as in the case of common approaches, nor additional efforts to obtain IPs, as in the case of equation-of-motion approaches. Overall, we believe that the present research may open new avenues in EA computations.

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<p>The coupled-cluster (CC) singles and doubles with perturbative triples [CCSD(T)] method is frequently referred to as the “gold standard" of modern computational chemistry. However, the high computational cost of CCSD(T) [O(N7)], where N is the number of basis functions, limits its applications to small-sized chemical systems. To address this problem, efficient implementations of linear-scaling coupled-cluster methods, which</p><p>employ the systematic molecular fragmentation (SMF) approach, are reported. In this study: (1) to achieve exact linear-scaling and to obtain a pure ab inito approach, we revise the handling of nonbonded interactions in the SMF approach (2) a new fragmentation algorithm, which yields smaller sized fragments; hence, better fits high-level CC methods is introduced (3) the new SMF approach is integrated with the high-level</p><p>CC methods, denoted by LSSMF-CC, for the first time. Performances of the LSSMF-CC approaches, such as LSSMF-CCSD(T), are compared with their canonical versions for a set of alkane molecules, CnH2n+2 (n=6–10), which includes 142 molecules. Our results demonstrate that the LSSMF approach introduces negligible errors compared with the canonical methods, mean absolute errors (MAEs) are between 0.20–0.59 kcal</p><p>mol-1 for LSSMF-CCSD(T). To further assess the accuracy of the LSSMF-CCSD(T) approach, we also consider several polyethylene (PE) models. For the PE set, the error of LSSMF-CCSD(T)/cc-pVDZ with respect to the experimental polymerization energies per unit are between 0.08–0.63 kcal/mol. To illustrate the efficiency and applicability of the LSSMF-CCSD(T) approach, we consider an alkane molecule with 10004 atoms. For this molecule, the LSSMF-CCSD(T)/cc-pVTZ energy computation on a Linux cluster with 100 nodes, 4 cores and 5 GB of memory are provided to each node, is performed just in ∼ 24 hours. As far as we know, this computation is an application of the CCSD(T) method on the largest chemical system to date. Overall, we conclude that (1) the LSSMF-CCSD(T) method can be reliably used for large scale chemical systems, where the canonical methods are not computationally affordable (2) the LSSMF-CCSD(T) method is very promising for accurate computation of energies in macromolecular systems (3) we believe that our study is a significant milestone in developing CC methods for large-scale chemical systems.</p>