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Setting Limits on Supersymmetry Using Simplified Models
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Models of necessity.

Timothy Clark1, Martin G Hicks2

  • 1Computer-Chemistry-Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-University Erlangen-Nürnberg, Nägelsbachstr. 25, 91052 Erlangen, Germany.

Beilstein Journal of Organic Chemistry
|August 1, 2020
PubMed
Summary
This summary is machine-generated.

Chemists use a simplified language of atoms and bonds to represent molecules, which is crucial for chemical cognition and computation. Artificial intelligence and machine learning will help extend these bonding models for better chemical predictions.

Keywords:
chemical bondingchemical ontologieschemical structure formatschemical structure modelschemical structure representationlanguage of chemistryquantum chemistry

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Area of Science:

  • Chemical Information Science
  • Computational Chemistry
  • Theoretical Chemistry

Background:

  • The everyday language of chemistry relies on simplified models of chemical structures, particularly bonding, which are not explicitly present in quantum mechanical descriptions.
  • These models, while effective for communication and computation, are often 'fuzzy' and vary among chemists, leading to occasional debates about their validity.

Purpose of the Study:

  • To explore the role of artificial intelligence (AI) and machine learning (ML) in extending and standardizing chemical bonding models.
  • To address the need for more comprehensive and computationally efficient models required for AI/ML applications in chemistry.

Main Methods:

  • The study discusses the conceptual framework for extending existing bonding models like Lewis and VSEPR.
  • It highlights the requirements for these extended models, including completeness, compactness, computational efficiency, and non-redundancy, driven by AI/ML applications.

Main Results:

  • AI and ML are poised to play a significant role in rationalizing, extending, and standardizing chemical bonding models.
  • These advancements are expected to enhance the predictive power of chemistry by providing more robust and universally applicable models.

Conclusions:

  • While AI and ML will refine chemical bonding models, they are unlikely to alter the fundamental 'everyday language' of chemistry.
  • These technologies may, however, provide deeper insights into the unique foundational basis of chemical language and representation.