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Eric D Glendening1, Clark R Landis2, Frank Weinhold2

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Natural resonance theory (NRT) offers a powerful computational approach to understanding chemical bonding. Recent advances in convex programming enable efficient calculation of resonance weightings, improving predictions for diverse chemical phenomena.

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Area of Science:

  • Quantum Chemistry
  • Chemical Bonding Theory
  • Computational Chemistry

Background:

  • Resonance theory has a complex history, evolving from diverse physics and chemistry concepts.
  • It became the dominant chemical bonding paradigm in the mid-20th century.
  • An alternative
  • natural
  • pathway led to localized Lewis- and resonance-structural concepts.

Purpose of the Study:

  • To outline the development of natural resonance theory (NRT).
  • To highlight recent computational advances in NRT.
  • To demonstrate NRT's applicability to various chemical systems.

Main Methods:

  • Development of semi-empirical and ab initio formulations of NRT.
  • Generalization to multi-structural NRT in the 1990s.
  • Application of efficient convex programming methods to solve the NRT variational problem.

Main Results:

  • Convex programming provides provably optimal resonance weightings at reduced computational cost.
  • NRT methodology is now "rebooted" for broad chemical applications.
  • Illustrative applications include stable species, transition states, inorganic interactions, and delocalization phenomena.

Conclusions:

  • Efficient convex programming methods have overcome previous numerical limitations in NRT.
  • NRT is now a versatile tool for studying resonance phenomena across chemistry.
  • The methodology is applicable to diverse systems, from organic molecules to inorganic materials.