scholarly journals Characterizing and Understanding the Remarkably Slow Basis Set Convergence of Several Minnesota Density Functionals for Intermolecular Interaction Energies

2013 ◽  
Vol 9 (10) ◽  
pp. 4453-4461 ◽  
Author(s):  
Narbe Mardirossian ◽  
Martin Head-Gordon
Keyword(s):  
Intermolecular Interaction ◽  
Basis Set ◽  
Density Functionals ◽  
Interaction Energies ◽  
Set Convergence

scholarly journals Dataset of Noncovalent Intermolecular Interaction Energy Curves for 24 Small High-Spin Open-Shell Dimers

2021 ◽  
Author(s):  
Katarzyna Madajczyk ◽  
Piotr Zuchowski ◽  
Filip Brzęk ◽  
Łukasz Rajchel ◽  
Dariusz Kędziera ◽  
...  
Keyword(s):  
Interaction Energy ◽  
Intermolecular Interaction ◽  
High Spin ◽  
Open Shell ◽  
Basis Set ◽  
Interaction Energies ◽  
Intermolecular Interaction Energy ◽  
Complete Basis ◽  
Complete Basis Set ◽  
Basis Set Extrapolation

<div>We introduce a dataset of 24 interaction energy curves of open-shell noncovalent dimers, referred to as the O24x5 dataset. The dataset consists of high-spin dimers up to eleven atoms selected to assure diversity with respect to interactions types: dispersion, electrostatics and induction. The benchmark interaction energies are obtained at the restricted open-shell CCSD(T) level of theory with complete basis set extrapolation aug-cc-pVQZ--> aug-cc-pV5Z.</div>

scholarly journals Dataset of Noncovalent Intermolecular Interaction Energy Curves for 24 Small High-Spin Open-Shell Dimers

2021 ◽  
Author(s):  
Katarzyna Madajczyk ◽  
Piotr Zuchowski ◽  
Filip Brzęk ◽  
Łukasz Rajchel ◽  
Dariusz Kędziera ◽  
...  
Keyword(s):  
Interaction Energy ◽  
Intermolecular Interaction ◽  
High Spin ◽  
Open Shell ◽  
Basis Set ◽  
Interaction Energies ◽  
Intermolecular Interaction Energy ◽  
Complete Basis ◽  
Complete Basis Set ◽  
Basis Set Extrapolation

<div>We introduce a dataset of 24 interaction energy curves of open-shell noncovalent dimers, referred to as the O24x5 dataset. The dataset consists of high-spin dimers up to eleven atoms selected to assure diversity with respect to interactions types: dispersion, electrostatics and induction. The benchmark interaction energies are obtained at the restricted open-shell CCSD(T) level of theory with complete basis set extrapolation aug-cc-pVQZ--> aug-cc-pV5Z.</div>

Basis Set Convergence of the Post-CCSD(T) Contribution to Noncovalent Interaction Energies

2014 ◽  
Vol 10 (8) ◽  
pp. 3140-3150 ◽  
Author(s):  
Daniel G. A. Smith ◽  
Piotr Jankowski ◽  
Michał Slawik ◽  
Henryk A. Witek ◽  
Konrad Patkowski
Keyword(s):  
Noncovalent Interaction ◽  
Basis Set ◽  
Interaction Energies ◽  
Set Convergence

Molecular Tailoring Approach for Estimating Individual Intermolecular Interaction Energies in Benzene Clusters

2021 ◽  
Author(s):  
Mini Bharati Ahirwar ◽  
Nalini D. Gurav ◽  
Shridhar R. Gadre ◽  
Milind M. Deshmukh
Keyword(s):  
Intermolecular Interaction ◽  
Interaction Energies ◽  
Molecular Tailoring

scholarly journals Intermolecular interaction energies in transition metal coordination compounds

CrystEngComm ◽  
2015 ◽  
Vol 17 (48) ◽  
pp. 9300-9310 ◽  
Author(s):  
Andrew G. P. Maloney ◽  
Peter A. Wood ◽  
Simon Parsons
Keyword(s):  
Transition Metal ◽  
Transition Metals ◽  
Crystal Structures ◽  
Intermolecular Interaction ◽  
Coordination Compounds ◽  
Metal Coordination ◽  
Interaction Energies

The PIXEL method has been parameterised and validated for transition metals, extending its applicability from ~40% to ~85% of all published crystal structures.

The assessment of density functionals for DNA–protein stacked and T-shaped complexes

2010 ◽  
Vol 88 (8) ◽  
pp. 815-830 ◽  
Author(s):  
Lesley R. Rutledge ◽  
Stacey D. Wetmore
Keyword(s):  
Amino Acid ◽  
Potential Energy ◽  
Large Scale ◽  
Enzymatic Reaction ◽  
Basis Sets ◽  
Density Functionals ◽  
Interaction Energies ◽  
Π Interactions ◽  
Hybrid Techniques ◽  
Semi Empirical

The present work uses 129 nucleobase – amino acid CCSD(T)/CBS stacking and T-shaped interaction energies as reference data to test the ability of various density functionals with double-zeta quality basis sets, as well as some semi-empirical and molecular mechanics methods, to accurately describe noncovalent DNA–protein π–π and π+–π interactions. The goal of this work is to identify methods that can be used in hybrid approaches (QM/MM, ONIOM) for large-scale modeling of enzymatic systems involving active-site (substrate) π–π contacts. Our results indicate that AMBER is a more appropriate choice for the lower-level method in hybrid techniques than popular semi-empirical methods (AM1, PM3), and suggest that AMBER accurately describes the π–π interactions found throughout DNA–protein complexes. The M06–2X and PBE-D density functionals were found to provide very promising descriptions of the 129 nucleobase – amino acid interaction energies, which suggests that these may be the most suitable methods for describing high-level regions. Therefore, M06–2X and PBE-D with both the 6–31G(d) and 6–31+G(d,p) basis sets were further examined through potential-energy surface scans to better understand how these techniques describe DNA–protein π–π interactions in both minimum and nonminimum regions of the potential-energy surfaces, which is critical information when modeling enzymatic reaction pathways. Our results suggest that studies of stacked nucleobase – amino acid systems should implement the PBE-D/6–31+G(d,p) method. However, if T-shaped contacts are involved and (or) smaller basis sets must be considered due to limitations in computational resources, then M06–2X/6–31G(d) provides an overall excellent description of both nucleobase – amino acid stacking and T-shaped interactions for a range of DNA–protein π–π and π+–π interactions.

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