Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models.

Abstract: Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and p Impact statement: Tannic acid is a versatile bio-derived material employed in coatings, surface modifiers, and emulsion and growth stabilizers, which also imparts mild anti-viral health benefits. Our recent work on the crystallization of oxidized tannic acid (OTA) colloids opens the route toward further valuable applications, but here the functional properties tend to depend strongly on particle morphology. In this study, we eschew trial-and-error morphology exploration of OTA particles in favor of a data-driven approach. We digitalized the experimental observations and input them into a Gaussian process regression algorithm to generate morphology surrogate models. These help us to visualize particle morphology in the design space of material processing conditions, and thus determine how to selectively engineer one-dimensional or three-dimensional particles with targeted functionalities. We extend this approach to visualize other experimental outcomes, including particle yield and particle surface-to-volume ratio, which are useful for the design of products based on OTA particles. Our findings demonstrate the use of data-efficient surrogate models for general materials engineering purposes and facilitate the development of next-generation OTA-based applications. Graphic abstract: Supplementary information: The online version contains supplementary material available at 10.1557/s43577-021-00183-4.