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S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures

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Řezáč, Jan, Riley, Kevin E., Hobza, Pavel·2011·Journal of Chemical Theory and Computation

DOI 10.1021/ct2002946

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Abstract

With numerous new quantum chemistry methods being developed in recent years and the promise of even more new methods to be developed in the near future, it is clearly critical that highly accurate, well-balanced, reference data for many different atomic and molecular properties be available for the parametrization and validation of these methods. One area of research that is of particular importance in many areas of chemistry, biology, and material science is the study of noncovalent interactions. Because these interactions are often strongly influenced by correlation effects, it is necessary to use computationally expensive high-order wave function methods to describe them accurately. Here, we present a large new database of interaction energies calculated using an accurate CCSD(T)/CBS scheme. Data are presented for 66 molecular complexes, at their reference equilibrium geometries and at 8 points systematically exploring their dissociation curves; in total, the database contains 594 points: 66 at equilibrium geometries, and 528 in dissociation curves. The data set is designed to cover the most common types of noncovalent interactions in biomolecules, while keeping a balanced representation of dispersion and electrostatic contributions. The data set is therefore well suited for testing and development of methods applicable to bioorganic systems. In addition to the benchmark CCSD(T) results, we also provide decompositions of the interaction energies by means of DFT-SAPT calculations. The data set was used to test several correlated QM methods, including those parametrized specifically for noncovalent interactions. Among these, the SCS-MI-CCSD method outperforms all other tested methods, with a root-mean-square error of 0.08 kcal/mol for the S66 data set.

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Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen
Dunning, T. H. (1989) · The Journal of Chemical Physics · doi:10.1063/1.456153 · Cited by 31,648
Fully optimized contracted Gaussian basis sets for atoms Li to Kr
Schäfer, A., Horn, H., & Ahlrichs, R. (1992) · The Journal of Chemical Physics · doi:10.1063/1.463096 · Cited by 9,610
Density Functionals with Broad Applicability in Chemistry
Zhao, Y., & Truhlar, D. G. (2008) · Accounts of Chemical Research · doi:10.1021/ar700111a · Cited by 7,133
Climbing the Density Functional Ladder: Nonempirical Meta–Generalized Gradient Approximation Designed for Molecules and Solids
Tao, J., Perdew, J. P., Staroverov, V. N., & Scuseria, G. E. (2003) · Physical Review Letters · doi:10.1103/physrevlett.91.146401 · Cited by 7,093
Gaussian basis sets for use in correlated molecular calculations. IV. Calculation of static electrical response properties
Woon, D. E., & Dunning, T. H. (1994) · The Journal of Chemical Physics · doi:10.1063/1.466439 · Cited by 2,618
Basis-set convergence in correlated calculations on Ne, N2, and H2O
Halkier, A., Helgaker, T., Jørgensen, P., Klopper, W., Koch, H., Olsen, J., & Wilson, A. K. (1998) · Chemical Physics Letters · doi:10.1016/s0009-2614(98)00111-0 · Cited by 2,192
Benchmark database of accurate (MP2 and CCSD(T) complete basis set limit) interaction energies of small model complexes, DNA base pairs, and amino acid pairs
Jurečka, P., Šponer, J., Černý, J., & Hobza, P. (2006) · Phys. Chem. Chem. Phys. · doi:10.1039/b600027d · Cited by 1,852
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Improved second-order Møller–Plesset perturbation theory by separate scaling of parallel- and antiparallel-spin pair correlation energies
Grimme, S. (2003) · The Journal of Chemical Physics · doi:10.1063/1.1569242 · Cited by 1,746
Design of Density Functionals That Are Broadly Accurate for Thermochemistry, Thermochemical Kinetics, and Nonbonded Interactions
Zhao, Y., & Truhlar, D. G. (2005) · The Journal of Physical Chemistry A · doi:10.1021/jp050536c · Cited by 1,708
DFTB+, a Sparse Matrix-Based Implementation of the DFTB Method
Aradi, B., Hourahine, B., & Frauenheim, T. (2007) · The Journal of Physical Chemistry A · doi:10.1021/jp070186p · Cited by 1,693

Show all 46 references

Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
Mardirossian, N., & Head-Gordon, M. (2017) · Molecular Physics · doi:10.1080/00268976.2017.1333644 · Cited by 2,307
Supramolecular Binding Thermodynamics by Dispersion‐Corrected Density Functional Theory
Grimme, S. (2012) · Chemistry – A European Journal · doi:10.1002/chem.201200497 · Cited by 2,111
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions
Goerigk, L., Hansen, A., Bauer, C., Ehrlich, S., Najibi, A., & Grimme, S. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp04913g · Cited by 1,894
Dispersion-Corrected Mean-Field Electronic Structure Methods
Grimme, S., Hansen, A., Brandenburg, J. G., & Bannwarth, C. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00533 · Cited by 1,621
Recent developments in the general atomic and molecular electronic structure system
Barca, G. M. J., Bertoni, C., Carrington, L., Datta, D., De Silva, N., Deustua, J. E., Fedorov, D. G., Gour, J. R., Gunina, A. O., Guidez, E., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0005188 · Cited by 1,443
TD-DFT benchmarks: A review
Laurent, A. D., & Jacquemin, D. (2013) · International Journal of Quantum Chemistry · doi:10.1002/qua.24438 · Cited by 1,382
ωB97X-V: A 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy
Mardirossian, N., & Head-Gordon, M. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c3cp54374a · Cited by 986
A geometrical correction for the inter- and intra-molecular basis set superposition error in Hartree-Fock and density functional theory calculations for large systems
Kruse, H., & Grimme, S. (2012) · The Journal of Chemical Physics · doi:10.1063/1.3700154 · Cited by 890
B97-3c: A revised low-cost variant of the B97-D density functional method
Brandenburg, J. G., Bannwarth, C., Hansen, A., & Grimme, S. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5012601 · Cited by 760
Exploring the Accuracy Limits of Local Pair Natural Orbital Coupled-Cluster Theory
Liakos, D. G., Sparta, M., Kesharwani, M. K., Martin, J. M. L., & Neese, F. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct501129s · Cited by 697
First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications
Hermann, J., DiStasio, R. A., & Tkatchenko, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00446 · Cited by 657
A foundation model for atomistic materials chemistry
Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., et al. (2024) · arXiv · doi:10.48550/arxiv.2401.00096 · Cited by 604
An improved chain of spheres for exchange algorithm
Helmich-Paris, B., de Souza, B., Neese, F., & Izsák, R. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0058766 · Cited by 573
A foundation model for atomistic materials chemistry
Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., et al. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0297006 · Cited by 566
Accurate and Efficient Model Energies for Exploring Intermolecular Interactions in Molecular Crystals
Turner, M. J., Grabowsky, S., Jayatilaka, D., & Spackman, M. A. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz502271c · Cited by 549
Revised Damping Parameters for the D3 Dispersion Correction to Density Functional Theory
Smith, D. G. A., Burns, L. A., Patkowski, K., & Sherrill, C. D. (2016) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6b00780 · Cited by 532
Minimally Empirical Double-Hybrid Functionals Trained against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4
Santra, G., Sylvetsky, N., & Martin, J. M. L. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b03157 · Cited by 517
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Christensen, A. S., Kubař, T., Cui, Q., & Elstner, M. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00584 · Cited by 446
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Risthaus, T., & Grimme, S. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct301081n · Cited by 423
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Erba, A., Desmarais, J. K., Casassa, S., Civalleri, B., Donà, L., Bush, I. J., Searle, B., Maschio, L., Edith-Daga, L., Cossard, A., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00958 · Cited by 367
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Marshall, M. S., Burns, L. A., & Sherrill, C. D. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3659142 · Cited by 351
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Authors: Řezáč, J., Riley, K. E., & Hobza, P.
Venue: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Published: 2011-07-19
Volume: 7
Issue: 8
Pages: 2427-2438
ISSN: 1549-9618 (ISSN-L)
1549-9626
DOI: 10.1021/ct2002946
OpenAlex: W2018120102
Cited by: 1,019
References: 46
FWCI: 35.97
Type: journal article
Language: en
Open access: hybrid

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S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures

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