Machine Learning for Biomaterials

Biomaterials play a crucial role in our pursuit of a sustainable society. Feedstock from biomass (e.g., wood) processed in biorefineries can provide us with as a renewable source of materials such as chemicals, solvents, and polymers that can subsequently be incorporated into high-value products. Bio materials furthermore offer alternative routes for waste management through biodegradation processes and promote equality in the global economy by decreasing our reliance on scarce raw materials.

To accelerate the development of new technologies for biomaterials, we are researching machine learning-assisted approaches to materials processing and modelling. Our current efforts focus on applying Bayesian optimization, through our in-house developed code BOSS, as a means of planning experiments and predicting their outcome.

For an application of machine learning and BOSS to optimize a novel biorefinery concept for green lignin extraction based on hydrothermal pre-treatment of hardwood followed by aqueous-acetone extraction see:

Machine Learning Optimization of Lignin Properties in Green Biorefineries. J. Löfgren, D. Tarasov, T. Koitto, P. Rinke, M. Balakshin, and M. Todorović, ACS Sustainable Chem. Eng. (2022)
 

For an application of machine learning and BOSS to predict the morphology of colloidal, oxidized tannic acid particles see:

Machine Learning as a Tool to Engineer Microstructures: Morphological Prediction of Tannin-Based Colloids Using Bayesian Surrogate Models. S.-A. Jin, T. Kämäräinen, P. Rinke, O. J. Rojas, and M. Todorovic, MRS Bulletin (2022)