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Related Experiment Video

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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

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

Soo-Ah Jin¹, Tero Kämäräinen², Patrick Rinke³

¹Department of Chemical & Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695 USA.

MRS Bulletin

|March 7, 2022

Summary

Machine learning (ML) was used to engineer oxidized tannic acid (OTA) particles with controlled shapes and sizes. This data-driven approach enables precise control over particle morphology for enhanced applications.

Keywords:

Gaussian process regression Morphology prediction Tannic acid

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Area of Science:

Materials Science
Biomaterials Engineering
Computational Chemistry

Background:

Oxidized tannic acid (OTA) is a versatile biomolecule forming complexes with metals and proteins.
OTA's functional properties are strongly dependent on particle morphology.
Traditional methods for exploring OTA particle morphology are inefficient.

Purpose of the Study:

To develop a data-driven approach for engineering oxidized tannic acid (OTA) particles.
To utilize machine learning (ML) for selective control over OTA particle morphology (1D to 3D).
To establish predictive models for OTA particle properties and guide material design.

Main Methods:

Employing Bayesian regression and Gaussian process regression for surrogate modeling.
Correlating colloidal suspension conditions (pH, pKa) with particle size and shape.
Digitalizing experimental observations for ML model input.

Main Results:

Developed chemically interpretable ML models for OTA morphology prediction.
Successfully engineered 1D and 3D OTA particles with targeted functionalities.
Generated property landscapes to satisfy multiple design objectives simultaneously.

Conclusions:

Data-efficient ML surrogate models offer powerful tools for materials engineering.
This approach facilitates the development of next-generation OTA-based applications.
Enables precise control over particle morphogenesis for diverse functionalities like bioactivity and stabilization.