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An Orbital-Overlap Complement to Atomic Partial Charge.

Arshad Mehmood¹, Benjamin G Janesko¹

¹Department of Chemistry and Biochemistry, Texas Christian University, 2800 S. University Drive, Fort Worth, TX, 76129, USA.

Angewandte Chemie (International Ed. in English)

|May 10, 2017

Summary

This summary is machine-generated.

This study introduces atomic overlap distance as a complementary metric to atomic partial charges for predicting chemical reactivity. Combining these two measures enhances predictions for molecular properties and material stability.

Keywords:

aromaticity atomic charge overlap distance regioselectivity substituent effects

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Area of Science:

Computational Chemistry
Quantum Chemistry
Materials Science

Background:

Atomic partial charges are commonly used to predict chemical reactivity.
Partial charge alone is insufficient, as demonstrated by atoms with similar charges but differing reactivities (e.g., carbons in benzene, diamond, graphene, C60).

Purpose of the Study:

To introduce and validate a new metric, atomic overlap distance, to complement atomic partial charges.
To demonstrate the combined predictive power of atomic charges and overlap distances for chemical properties.

Main Methods:

Calculation of atomic partial charges.
Computation of atomic overlap distance, measuring orbital lobe size overlapping with atomic wavefunctions.
Application of combined metrics to analyze trends in aromaticity, nucleophilicity, and allotrope stability.

Main Results:

Atomic overlap distance correlates with atomic stability and softness.
The combination of atomic charges and overlap distances accurately captures trends in aromaticity, nucleophilicity, allotrope stability, and substituent effects.
Successful application to experimental cases in organic and nanomaterials chemistry, including Lewis base stabilization and doping of gold clusters.

Conclusions:

Atomic overlap distance provides crucial insights into chemical reactivity beyond partial charges.
The combined charge-overlap distance approach offers enhanced predictive power for diverse chemical systems.
This method has significant implications for understanding and designing novel molecules and materials.