Can we predict interface dipoles based on molecular properties?

TL;DR

This paper explores the prediction of interface dipoles from molecular properties using high-throughput DFT calculations and symbolic regression, revealing both physically meaningful and unphysical correlations in different interface scenarios.

Contribution

It demonstrates the application of symbolic regression to identify correlations between molecular properties and interface dipoles, validating known models and uncovering new insights.

Findings

Validation of the Topping model for free-standing monolayers

Discovery of highly accurate but unphysical correlations in charge-transfer interfaces

Differentiation between physically meaningful and unphysical predictive models

Abstract

We apply high-throughput DFT calculations and symbolic regression to hybrid inorganic/organic interfaces with the intent to extract physically meaningful correlations between the adsorption-induced work function modifications and the properties of the constituents. We separately investigate two cases: Hypothetical, free standing self-assembled monolayers with a large intrinsic dipole moment, and metal-organic interfaces with a large charge-transfer induced dipole. For the former we find - without notable prior assumptions - the Topping model, as expected from literature. For the latter, highly accurate correlations are found, which are, however, clearly unphysical.