Abstract
Dye adsorption on metal-oxide films often results in small to substantial absorption shifts relative to the solution phase, with undesirable consequences for the performance of dye-sensitized solar cells and optical sensors. While density functional theory is frequently used to model such behaviour, it is too time-consuming for rapid assessment. In this paper, we explore the use of supervised machine learning to predict whether dye adsorption on titania is likely to induce a change in its absorption characteristics. The physicochemical features of each dye were encoded as a numeric vector whose elements are the counts of molecular fragments and topological indices. Various classification models were subsequently trained to predict the type of absorption shift i.e. blue, red or unchanged (|Δλ| ≤ 10 nm). The models were able to predict the nature of the shift with a good likelihood (~80%) of success when applied to unseen data.
Highlights
The light-harvesting properties of dye-sensitized metal oxides find a number of applications in photonic devices and chemical probes
Broad absorption spectra are desirable for light harvesting
We ask the question: based only on the knowledge of a dye molecule's chemical structure and its absorption spectrum in a given solvent, can we use data-driven Machine learning (ML) techniques to predict the type of absorption shift? To this purpose, the UV-Vis absorption peaks in solution and on a metal oxide were extracted from literature for ~2000 dyes
Summary
The light-harvesting properties of dye-sensitized metal oxides find a number of applications in photonic devices and chemical probes. We ask the question: based only on the knowledge of a dye molecule's chemical structure and its absorption spectrum in a given solvent, can we use data-driven ML techniques to predict the type of absorption shift? Supervised machine learning models were trained to distinguish between the classes using descriptors such as molecular fragment counts and topological indices that are calculated from the dye structure.
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