benchmarks
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benchmarks [2013/10/04 14:40] nuno [Reaction Pathways] |
benchmarks [2017/06/12 16:16] (current) nuno [Introduction] |
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| ====== Introduction ====== | ====== Introduction ====== | ||
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| - | Several Density Functional Theory (DFT) benchmarks have been performed by our group. DFT is less demanding than other computational methods with similar accuracy; and is able to include electron correlation in the calculations at a fraction of the time of post-Hartree-Fock methodologies. It also permits the study of molecular systems containing up to 200 atoms, a feature that is not yet feasible with high accuracy methods such as CCSD(T) or even secondorder Möller-Plesset perturbation theory (MP2). Presently, no universally and transferable selection of DFT functionals can be employed, rendering the choice of DFT methodologies, highly influenced by the problem at hand. Consequently, an extensive evaluation of the performance of several density functionals in different fronts (structure, kinetics, thermochemistry, and nonbonded interactions) has emerged in the literature throughout the years, and is crucial when developing a project based on these methodologies. | + | Several Density Functional Theory (DFT) benchmarks have been performed by our group. DFT is less demanding than other computational methods with similar accuracy; and is able to include electron correlation in the calculations at a fraction of the time of post-Hartree-Fock methodologies. It also permits the study of molecular systems containing up to 200 atoms, a feature that is not yet feasible with high accuracy methods such as CCSD(T) or even secondorder Möller-Plesset perturbation theory (MP2). Presently, no universally and transferable selection of DFT functionals can be employed, rendering the choice of DFT methodologies, highly influenced by the problem at hand. |
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| - | In the present study, we have compared the performance of the density functional theory (DFT) functionals B1B95, B3LYP, B97-2, BP86, and BPW91 with MP2 for geometry determination in biological mononuclear | + | We have compared the performance of the density functional theory (DFT) functionals B1B95, B3LYP, B97-2, BP86, and BPW91 with MP2 for geometry determination in biological mononuclear Zn complexes. A total of 15 different basis sets, of rather diverse complexity, were tested, several which included also 3 different types of common effective-core potentials. In addition, the ability to describe mononuclear Zn biological systems using relatively simple models of the metal coordination sphere, comprising only the metal atom and a simplified representation of the ligands at the first coordination sphere, starting from a set of high-resolution X-ray crystallographic structures, is evaluated for 90 combinations of method/basis set. Globally, B3LYP had the best average performance in the test, closely followed by MP2. The study also points out B3LYP/CEP-121G and B3LYP/SDD, as the best compromise between accuracy and CPU time. |
| - | Zn complexes. A total of 15 different basis sets, of rather diverse complexity, were tested, several which | + | |
| - | included also three different types of common effective-core potentials. In addition, the ability to describe mononuclear Zn biological systems using relatively simple models of the metal coordination sphere, comprising only the metal atom and a simplified representation of the ligands at the first coordination sphere, starting from a set of high-resolution X-ray crystallographic structures, is evaluated for 90 combinations of method/basis set. Globally, B3LYP had the best average performance in the test, closely followed by MP2. The study also points out B3LYP/CEP-121G and B3LYP/SDD, as the best compromise between accuracy and CPU time for the geometrical characterization of metal-ligand bond lengths in Zn biological systems. | + | |
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| - | In this study, a set of 50 transition-metal complexes of Cu(I) and Cu(II), were used in the evaluation of 18 density functionals in geometry determination. In addition, 14 different basis sets were considered, including four commonly used Pople’s all-electron basis sets, four basis sets including popular types of effective-core potentials, and six triple-zeta basis sets. The results illustrate the performance of different methodological alternatives for the treatment of geometrical properties in relevant copper complexes, pointing out Double-Hybrid (DH) and Long-range Correction (LC) Generalized Gradient Approximation (GGA) methods as better descriptors of the geometry of the evaluated systems. These however, are associated with a computational cost several times higher than some of the other methods employed, such as the M06 functional, which has also demonstrated a comparable performance. In addition, the results show that the use of effective-core potentials has a limited impact, in terms of the accuracy in the determination of metal-ligand bondlengths and angles in our dataset of copper complexes. Hence, these could become a good alternative for the geometrical description of these systems. | + | In this study, a set of 50 transition-metal complexes of Cu(I) and Cu(II), were used in the evaluation of 18 density functionals in geometry determination. In addition, 14 different basis sets were considered. The results illustrate the performance of different methodological alternatives for the treatment of geometrical properties in relevant copper complexes, pointing out Double-Hybrid (DH) and Long-range Correction (LC) Generalized Gradient Approximation (GGA) methods as better descriptors of the geometry of the evaluated systems. These however, are associated with a computational cost several times higher than some of the other methods employed, such as the M06 functional, which has also demonstrated a comparable performance. In addition, the results show that the use of effective-core potentials has a limited impact, in terms of the accuracy in the determination of metal-ligand bondlengths and angles in our dataset of copper complexes. Hence, these could become a good alternative for the geometrical description of these systems. |
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| ==== Reaction Pathways ==== | ==== Reaction Pathways ==== | ||
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| - | Phosphodiester bonds are an important chemical component of biological systems, and their hydrolysis and formation reactions are involved in major steps throughout metabolic pathways of all organisms. In this work, we applied dimethylphosphate as a model for this kind of bonds and calculated the potential energy surface for its hydrolysis at the approximated CCSD(T)/CBS/B3LYP/6-311++G(2d,2p) level. By varying the nucleophile (water or hydroxide) and the medium (vacuum or aqueous implicit solvent) we obtained and described four reaction paths. These structures were then used in a DFT functional benchmarking in which we tested a total of 52 functionals. Furthermore, the performances of HF, MP2, MP3, MP4, and CCSD were also evaluated. This benchmarking showed that MPWB1K, MPW1B95, and PBE1PBE are the more accurate functionals to calculate the energies of dimethylphosphate hydrolysis as far as activation and reaction energies are concerned. If considering only the activation energies, MPWB1K, MPW1B95, and B1B95 give the lowest errors when comparing to CCSD(T). A basis set benchmarking on the same system shows that 6-311+G(2d,2p) is the best basis set concerning the relationship between computational time and accuracy. | + | We applied dimethylphosphate as a model for phosphodiester bonds and calculated the potential energy surface for its hydrolysis at the approximated CCSD(T)/CBS/B3LYP/6-311++G(2d,2p) level. By varying the nucleophile (water or hydroxide) and the medium (vacuum or aqueous implicit solvent) we obtained and described four reaction paths. These structures were then used in a DFT functional benchmarking in which we tested a total of 52 functionals. Furthermore, the performances of HF, MP2, MP3, MP4, and CCSD were also evaluated. This benchmarking showed that MPWB1K, MPW1B95, and PBE1PBE are the more accurate functionals to calculate the energies of dimethylphosphate hydrolysis as far as activation and reaction energies are concerned. If considering only the activation energies, MPWB1K, MPW1B95, and B1B95 give the lowest errors when comparing to CCSD(T). A basis set benchmarking on the same system shows that 6-311+G(2d,2p) is the best basis set concerning the relationship between computational time and accuracy. |
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| - | The ionization states of amino acids influence the structure, function, stability, solubility, and reactivity of proteins and are difficult to determine unambiguously by experimental means. Thus, it is very important to be able to determine them theoretically and with high reliability. We have analyzed how well DFT functionals, often used to characterize complex and large models such as proteins, describe the zero-point-exclusive proton affinity at 0 K, PAel0K, for the ionizable side chains of lysine (Lys), histidine (His), arginine (Arg), and aspartate (Asp–) as well as the cysteine (Cys–), serine (Ser–), and tyrosine (Tyr–) anions. The reference values PAel0K were determined at the very accurate CCSD(T)/CBS level. Those values were obtained by the sum of the complete basis set limit of the MP2 energies plus a CCSD(T) correction term evaluated with the aug-cc-pVTZ basis set. The complete basis set limit of MP2 energies was determined using the Truhlar and Helgaker extrapolation schemes. A new, important, and consistent DFT benchmarking database for PAel0K and for proton transfer between two different ionizable side chains, ΔPAel0K, is provided, making this work relevant to all studies with ionizable amino acids side chains that use DFT. Among the 64 density functionals tested, the MPW1B95-D3, XYG3, MPW1B95, B1B95-D3, BMK, BMK-D3, M06-2X, B1LYP, B1B95, PBE1PBE, CAM-B3LYP, B97-1, PBE1KCIS, B3P86, CAM-B3LYP-D3, B3LYP, B98, M06-L, and M06 provide the most accurate PAel0K values for all ionizable amino acids studied, with errors below 1.5 kcal/mol, which translate into an error of less than 1 pKa unit in solution. Furthermore, among the best rated to predict PAel0K, we have found that M06-2X was the most accurate density functional for proton transfers between different amino acids. | + | We have analyzed how well DFT functionals describe the zero-point-exclusive proton affinity at 0 K, PAel0K, for the ionizable side chains of lysine, histidine, arginine, and aspartate as well as the cysteine, serine, and tyrosine anions. The reference values PAel0K were determined at the very accurate CCSD(T)/CBS level. Those values were obtained by the sum of the complete basis set limit of the MP2 energies plus a CCSD(T) correction term evaluated with the aug-cc-pVTZ basis set. The complete basis set limit of MP2 energies was determined using the Truhlar and Helgaker extrapolation schemes. A new, important, and consistent DFT benchmarking database for PAel0K and for proton transfer between two different ionizable side chains, ΔPAel0K, is provided, making this work relevant to all studies with ionizable amino acids side chains that use DFT. Among the 64 density functionals tested, the MPW1B95-D3, XYG3, MPW1B95, B1B95-D3, BMK, BMK-D3, M06-2X, B1LYP, B1B95, PBE1PBE, CAM-B3LYP, B97-1, PBE1KCIS, B3P86, CAM-B3LYP-D3, B3LYP, B98, M06-L, and M06 provide the most accurate PAel0K values for all ionizable amino acids studied, with errors below 1.5 kcal/mol, which translate into an error of less than 1 pKa unit in solution. Furthermore, among the best rated to predict PAel0K, we have found that M06-2X was the most accurate density functional for proton transfers between different amino acids. |
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| Silva, P.J., Perez, M.A.S., Bras, N.F., Fernandes P.A., and Ramos M.J [[http://link.springer.com/article/10.1007%2Fs00214-012-1179-x | Improving the study of proton transfers between amino acid side chains in solution: choosing appropriate DFT functionals and avoiding hidden pitfalls]] . //Theor. Chem. Acc.//, **2012**, 131 (3), 1179 | Silva, P.J., Perez, M.A.S., Bras, N.F., Fernandes P.A., and Ramos M.J [[http://link.springer.com/article/10.1007%2Fs00214-012-1179-x | Improving the study of proton transfers between amino acid side chains in solution: choosing appropriate DFT functionals and avoiding hidden pitfalls]] . //Theor. Chem. Acc.//, **2012**, 131 (3), 1179 | ||
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benchmarks.1380894048.txt.gz · Last modified: 2013/10/04 14:40 by nuno
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