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Hard Numbers for Large Molecules: Toward Exact Energetics for Supramolecular Systems
Open accessAbstract
Noncovalent interactions are ubiquitous in molecular and condensed-phase environments, and hence a reliable theoretical description of these fundamental interactions could pave the way toward a more complete understanding of the microscopic underpinnings for a diverse set of systems in chemistry and biology. In this work, we demonstrate that recent algorithmic advances coupled to the availability of large-scale computational resources make the stochastic quantum Monte Carlo approach to solving the Schrödinger equation an optimal contender for attaining "chemical accuracy" (1 kcal/mol) in the binding energies of supramolecular complexes of chemical relevance. To illustrate this point, we considered a select set of seven host-guest complexes, representing the spectrum of noncovalent interactions, including dispersion or van der Waals forces, π-π stacking, hydrogen bonding, hydrophobic interactions, and electrostatic (ion-dipole) attraction. A detailed analysis of the interaction energies reveals that a complete theoretical description necessitates treatment of terms well beyond the standard London and Axilrod-Teller contributions to the van der Waals dispersion energy.
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Citations
Cited by
Gas Separation in Nanoporous Graphene from First Principle Calculations
Ambrosetti, A., & Silvestrelli, P. L. (2014) · The Journal of Physical Chemistry C · doi:10.1021/jp504914u · Cited by 68
Self-citationLi, S., Luo, Z., Wang, W., Sun, H., Xie, J., & Liang, X. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.123853 · Cited by 49
Metadata
- Authors
- Ambrosetti, A., Alfè, D., DiStasio, R. A., & Tkatchenko, A.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-02-17
- Volume
- 5
- Issue
- 5
- Pages
- 849-855
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W2061311518
- Cited by
- 189
- References
- 0
- FWCI
- 14.31
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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