Abstract

The everyday language of chemistry uses models, particularly of bonding, that are not contained in the quantum mechanical description of chemical systems. To date, this everyday language has overlapped strongly with that (the ontology) of artificial intelligence (AI) and machine learning (ML). Within the everyday language, the model nature of these concepts is not always clear to practicing chemists, so that controversial discussions about the merits of alternative models often arise. However, the extensive use of AI and ML in chemistry will require that these models be extended to cover all relevant properties and characteristics of chemical systems. This in turn imposes conditions such as completeness, compactness, computational efficiency and non-redundancy on the extensions to the almost universal Lewis and VSEPR bonding models. Thus, AI and ML are likely to be important in rationalizing and standardizing chemical bonding models. This will not affect the everyday language of chemistry but may help understand the unique basis of chemical language.

Keywords: Models in chemistry; Lewis structures; VSEPR; quantum chemistry; Artificial Intelligence; Machine Learning

How to Cite

When a peer-reviewed version of this preprint is available, this information will be updated in the information box above. If no peer-reviewed version is available, please cite this preprint using the following information:

Clark, T.; Hicks, M. G. Beilstein Arch. 2020, 202077. doi:10.3762/bxiv.2020.77.v1

Download Citation

Citation data can be downloaded as file using the "Download" button or used for copy/paste from the text window below.
Citation data in RIS format can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Zotero.