Dimensional Analysis of Lewis Acidity: A Theoretical, Experimental, and Data Driven Approach

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The concept of Lewis acids was formulated over one hundred years ago. Despite its extreme qualitative value, there is still no satisfactory model for a quantitative understanding of the "strength" of a Lewis acid. While some trends can be described by the principle of hard and soft acids, numerous exceptions point to significant weaknesses in this empirical approach. While in earlier eras, such and similar models were based on limited experimental data, modern computational chemistry or analytical methods allow the generation of affinity data on a substantially larger scale (factor > 1000). This project aims to investigate Lewis acidity by statistical tools. The starting point is the generation of a dataset with approximately 30,000 affinities. A machine learning model is generated by suitable training data sets, which can predict the affinity of a compound toward selected donors based on its two-dimensional Lewis structure as the only input. In parallel to these theoretical approaches, and to avoid losing sight of effects in the condensed phase, thermodynamic data on Lewis pair formation are obtained by isothermal calorimetry (ITC). Establishing ITC operation under inert gas conditions represents a novelty in inorganic molecular chemistry. In this way, experimental affinity data of around 500 Lewis pairs will become accessible, which can serve as a benchmark for the theoretical methods from the first subproject. Finally, the collected theoretical and experimental affinities of Lewis acids toward numerous Lewis bases are treated via dimensionality reduction and related methods. For example, with principal component analysis, a statement can be made on how many and which affinity scales are needed to reflect as much information as possible about a Lewis acid. By factor analysis, a physicochemical interpretation emerges as to which factors are significantly responsible for the affinity of a Lewis acid. What is the dimensionality of the Lewis acid problem? To our knowledge, this research represents the first "big data" approach to appraise this centuries-old concept. We anticipate critical knowledge gains with implications in catalysis, materials research, and the life sciences.