Dataset Information

Assessment of the CCSD and CCSD(T) Coupled-Cluster Methods in Calculating Heats of Formation for Cu Complexes.

ABSTRACT: Heats of formation for nine complexes of the form CuX(n) (X = Cu, H, O, OH, S, F, F(2), Cl, Cl(2)) were calculated using the CCSD and CCSD(T) coupled cluster methods with the 6-31G** and TZVP basis sets as well as the LANL2DZ basis set/pseudopotential on Cu with both the 6-31G** and TZVP basis sets applied to the nonmetal atoms. These values were compared with literature heat of formation values. A second order Douglas-Kroll-Hess relativistic correction was applied at the CCSD/TZVP and CCSD(T)/TZVP levels of theory. Overall, the CCSD(T)/TZVP level of theory with the relativistic correction was most suited for the heat of formation calculations possessing low absolute average error and RMSD and the ability to analyze each copper complex, except for the problematic case of copper(II) fluoride. Finally, experimental geometric parameters were compared with the calculated structures in such cases where these data were available. None of the investigated levels of theory predicted bond lengths consistently better than other methods, and it was determined that the most accurate bond length does not necessarily result in the most accurate calculated heat of formation value for a given complex.

SUBMITTER: Weaver MN

PROVIDER: S-EPMC2885807 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessment of the CCSD and CCSD(T) Coupled-Cluster Methods in Calculating Heats of Formation for Cu Complexes.

Weaver Michael N MN Merz Kenneth M KM

Molecular physics 20090101 8-12

Heats of formation for nine complexes of the form CuX(n) (X = Cu, H, O, OH, S, F, F(2), Cl, Cl(2)) were calculated using the CCSD and CCSD(T) coupled cluster methods with the 6-31G** and TZVP basis sets as well as the LANL2DZ basis set/pseudopotential on Cu with both the 6-31G** and TZVP basis sets applied to the nonmetal atoms. These values were compared with literature heat of formation values. A second order Douglas-Kroll-Hess relativistic correction was applied at the CCSD/TZVP and CCSD(T)/T ...[more]

PMID: 20556234

Similar Datasets

Project description:BACKGROUND:Symptom clusters are conventionally distilled into a single score using composite scoring, which is based on the mathematical assumption that all symptoms are equivalently related to outcomes of interest; this may lead to a loss of important variation in the data. OBJECTIVES:This article compares two ways of calculating a single score for a symptom cluster: a conventional, hypothesis-driven composite score versus a data-driven, reduced rank regression score that weights the symptoms based on their individual relationships with key outcomes. METHODS:We conducted a secondary analysis of psychoneurological symptoms from a sample of 356 low-income mothers. Four of the psychoneurological symptoms (fatigue, cognitive dysfunction, sleep disturbance, and depressed mood) were measured with the Center for Epidemiological Studies Depression Scale; the fifth (pain) was measured using an item from the Medical Outcomes Study 12-item Short Form Health Survey (SF-12). Mothers' function was measured using the 12-item Short Form Health Survey. The composite score was calculated by summing standardized scores for each individual psychoneurological symptom. In contrast, reduced rank regression weighted the individual symptoms using their respective associations with mothers' function; the weighted individual symptom scores were summed into the reduced rank regression symptom score. RESULTS:The composite score and reduced rank regression score were highly correlated at .93. The cluster of psychoneurological symptoms accounted for 53.7% of the variation in the mothers' function. Depressed mood and pain accounted for almost all the explained variation in mothers' function at 37.2% and 15.0%, respectively. DISCUSSION:The composite score approach was simpler to calculate, and the high correlation with the reduced rank regression score indicates that the composite score reflected most of the variation explained by the reduced rank regression approach in this data set. However, the reduced rank regression analysis provided additional information by identifying pain and depressed mood as having the strongest association with a mother's function, which has implications for understanding which symptoms to target in future interventions. Future studies should also explore composite versus reduced rank regression approaches given that reduced rank regression may yield different insights in other data sets.

Project description:Computational chemists have long demonstrated great interest in finding ways to reliably and accurately predict the molecular properties for transition-metal-containing complexes. This study is a continuation of our validation efforts of density functional theory (DFT) methods when applied to transition-metal-containing systems (Riley, K.E.; Merz, K. M., Jr. J. Phys. Chem. 2007, 111, 6044-6053). In our previous work we examined DFT using all-electron basis sets, but approaches incorporating effective core potentials (ECPs) are effective in reducing computational expense. With this in mind, our efforts were expanded to include evaluation of the performance of the basis set derived to approximate such an approach as well on the same set of density functionals. Indeed, employing an ECP basis such as LANL2DZ (Los Alamos National Laboratory 2 double zeta) for transition metals, while using all-electron basis sets for all other non-transition-metal atoms, has become more and more popular in computations on transition-metal-containing systems. In this study, we assess the performance of 12 different DFT functionals, from the GGA (generalized gradient approximation), hybrid-GGA, meta-GGA, and hybrid-meta-GGA classes, respectively, along with the 6-31+G** + LANL2DZ (on the transition metal) mixed basis set in predicting two important molecular properties, heats of formation and ionization potentials, for 94 and 58 systems containing first-row transition metals from Ti to Zn, which are all in the third row of the periodic table. An interesting note is that the inclusion of the exact exchange term in density functional methods generally increases the accuracy of ionization potential prediction for the hybrid-GGA methods but decreases the reliability of determining the heats of formation for transition-metal-containing complexes for all hybrid density functional methods. The hybrid-GGA functional B3LYP gives the best performance in predicting the ionization potentials, while the meta-GGA functional TPSSTPSS provides the most reliable and accurate results for heat of formation calculations. TPSSTPSS, a meta-GGA functional, which was constructed from first principles and subject to known exact constraints just like in an "ab initio" way, is successful in predicting both the ionization potentials and the heats of formation for transition-metal-containing systems.

Project description: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 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, 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 and 0.59 kcal mol-1 for LSSMF(3,1)-CCSD(T). For a larger alkanes set (L12), CnH2n+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 be reliably used for large-scale chemical systems, where the canonical methods are not computationally affordable. (2) The accuracy of bonded level 3 is not satisfactory for large chemical systems. (3) For high-accuracy studies, bonded level 5 (or higher) and nonbonded level 2 should be employed.

Project description:Abstract Catch-and-release fishing is an important component of ecotourism industries and scientific research worldwide, but its total impact on animal physiology, health and survival is understudied for many species of fishes, particularly sharks. We combined biologging and blood chemistry to explore how this fisheries interaction influenced the physiology of two widely distributed, highly migratory shark species: the blue shark (Prionace glauca) and the tiger shark (Galeocerdo cuvier). Nineteen sharks were caught by drum line or rod-and-reel angling; subcutaneous body temperature measurements were taken immediately upon capture, with six individuals also providing subsequent subcutaneous body temperature measurements via biologging as they swam freely for several hours post-release. We found that short-term capture caused shark body temperature to increase significantly and rapidly, with increases of 0.6°C–2.7°C for blue sharks (mean, 1.2 ± 0.6°C) and 0.5°C–0.9°C for tiger sharks (mean, 0.7 ± 0.2°C) and with capture-induced heating rates of blue sharks averaging 0.3°C min−1 but as high as 0.8°C min−1. Blue shark body temperature was even higher deeper into the white muscle. These heating rates were three to eight times faster than maximum rates encountered by our biologging sharks swimming through thermally stratified waters and faster than most acute heating experiments conducted with ectotherms in laboratory experiments. Biologging data showed that body temperatures underwent gradual decline after release, returning to match water temperatures 10–40 mins post-release. Blood biochemistry showed variable lactate/glucose levels following capture; however, these concentrations were not correlated with the magnitude of body temperature increase, nor with body size or hooking time. These perturbations of the natural state could have immediate and longer-term effects on the welfare and ecology of sharks caught in catch-and-release fisheries and we encourage further study of the broader implications of this reported phenomenon.

Dataset Information

Assessment of the CCSD and CCSD(T) Coupled-Cluster Methods in Calculating Heats of Formation for Cu Complexes.

Publications

Assessment of the CCSD and CCSD(T) Coupled-Cluster Methods in Calculating Heats of Formation for Cu Complexes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets