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Covalent Attachment of Single Molecules for AFM-based Force Spectroscopy

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Published on: March 16, 2020

A benchmark for non-covalent interactions in solids.

A Otero-de-la-Roza¹, Erin R Johnson

¹Chemistry and Chemical Biology, School of Natural Sciences, University of California, Merced, 5200 North Lake Road, Merced, California 95343, USA. aoterodelaroza@ucmerced.edu

The Journal of Chemical Physics

|August 17, 2012

Summary

This summary is machine-generated.

This study presents a benchmark for non-covalent interactions in molecular crystals. The exchange-hole dipole moment (XDM) model significantly improves the accuracy of computed lattice energies and predicts crystal geometries effectively.

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Area of Science:

Solid-state chemistry
Computational materials science
Quantum chemistry

Background:

Accurate prediction of non-covalent interactions is crucial for understanding molecular solids.
Existing methods for calculating lattice energies and geometries have limitations.

Purpose of the Study:

To establish a benchmark for evaluating computational methods for non-covalent interactions in molecular crystals.
To assess the performance of the exchange-hole dipole moment (XDM) model for solid-state applications.

Main Methods:

Development of a benchmark dataset using experimental sublimation enthalpies and geometries of 21 molecular crystals.
Careful accounting for thermal and zero-point effects.
Comparison of the XDM model with DFT-D2 and other non-local functionals.

Main Results:

The XDM model demonstrated a significant improvement in accuracy for computed lattice energies, with a mean absolute error of 4.8 kJ/mol.
The XDM model accurately predicted cell geometries, with deviations less than 2% from experimental results.
The XDM model roughly doubled the accuracy compared to DFT-D2 and non-local functionals.

Conclusions:

The XDM model is a highly promising approach for accurately describing dispersion interactions in solid-state applications.
The established benchmark provides a valuable resource for assessing future computational methods.
Accurate prediction of crystal properties is essential for materials design and discovery.