Unicorns, Rhinoceroses and Chemical Bonds

Area of Science:

• Quantum chemistry
• Computational molecular design
• Materials science

Background:

• Chemical intuition, crucial for synthesizing novel molecules, relies on the concept of chemical bonds.
• Traditional computational chemistry often views chemical bonds as non-existent or mythical, posing a challenge for computation-aided molecular design.
• Existing methods struggle to quantify bond properties, hindering the integration of chemical intuition into computational approaches.

Purpose of the Study:

• To demonstrate that chemical bonds, often considered mythical in computational chemistry, can be rigorously defined and quantified.
• To introduce the concept of
• bond bundles
• as the real-space manifestation of chemical bonds within the Quantum Theory of Atoms in Molecules (QTAIM).
• To show that bond bundles possess properties analogous to traditional chemical bonds and offer new insights for molecular design.

Main Methods:

• Application of the Quantum Theory of Atoms in Molecules (QTAIM) formalism.
• Analysis of electron density and real-space properties to define and characterize bond bundles.
• Examination of examples from solid-state and molecular chemistry to validate the findings.

Main Results:

• Bond bundles are identified as the real-space equivalent of chemical bonds.
• Bond bundles exhibit quantifiable properties such as energy and electron count, consistent with chemical intuition.
• Bond bundles possess unique attributes, including a defined boundary, offering novel perspectives on molecular structure.
• Calculated bond bundle properties align with empirical observations in both molecular and solid-state systems.

Conclusions:

• The concept of bond bundles provides a robust, quantifiable foundation for chemical bonds within computational chemistry.
• This approach bridges the gap between theoretical calculations and the empirically based chemical intuition of synthetic chemists.
• Bond bundles offer new avenues for understanding molecular and material properties, advancing computationally aided molecular design.