Models of necessity

Abstract

The way chemists represent chemical structures as two-dimensional sketches made up of atoms and bonds, simplifying the complex three-dimensional molecules comprising nuclei and electrons of the quantum mechanical description, is the everyday language of chemistry. This language uses models, particularly of bonding, that are not contained in the quantum mechanical description of chemical systems, but has been used to derive machine-readable formats for storing and manipulating chemical structures in digital computers. This language is fuzzy and varies from chemist to chemist but has been astonishingly successful and perhaps contributes with its fuzziness to the success of chemistry. It is this creative imagination of chemical structures that has been fundamental to the cognition of chemistry and has allowed thought experiments to take place. 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 artificial intelligence (AI) and machine learning (ML) in chemistry, with the aim of being able to make reliable predictions, 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, extending and standardizing chemical bonding models. This will not affect the everyday language of chemistry but may help to understand the unique basis of chemical language.

How deeply should we analyze non-covalent interactions?

“…Interestingly, Mulliken's original discussion of CT occurred within a VB context [9]. Molecular orbital theory is not as lucky: Polarization and CT cannot be separated uniquely in calculations on intermolecular complexes, as has been pointed out many times [6,10]. QTAIM is in a unique position in this respect because it provides a unique definition of the separating surface between atoms, so that "atomistic" concepts such as CT are also well-defined.…”

“…A central point is to distinguish between chemical models, which are the essence of how we think about chemistry, and "physical reality," which I have put in quotation marks. Martin Hicks and I have considered the roles of models in chemistry in more detail in a recent essay [6]. In short, the majority of the concepts we use in chemistry go back to some sort of chemical model, usually a bonding model.…”

“…Tim Clark: Yes, Paul, emotions often run high in fields that are really a matter of interpretation. Martin Hicks and I have tried to emphasize the model nature of chemical thinking, [6] but with little resonance. Competing models take on religious traits.…”

Information Entropy in Chemistry: An Overview

“…This is due to the multilayered nature of chemical reality (term after G. Bachelard's philosophizing on chemical sciences [25]). Indeed, there are many modes to describe molecular structures that take into account empirical formulas, molecular graphs, geometry, or conformational dynamics [26][27][28][29]. The required level of detailing structure is completely attributed to the aim of the chemical study.…”

In vivo, in vitro and in silico: an open space for the development of microbe‐based applications of synthetic biology

“…To make the most of computer‐mediated exploration, it is critical that models are built on very precise definitions and cardinal foundations, not on fuzzy vocabulary. This topic, in particular, the need of accurate definitions, is discussed in details by Clark and Hicks in the field of chemistry, showing how the choice of models impacts the outcomes of research, including with modern AI approaches (Clark and Hicks, 2020). Because models rest on the collection of relevant data, the key question of standardization and nomenclature must be asked, with expedient approaches (Decoene et al ., 2018).…”