Project R-14811

QuantumLignin: Elucidating the interactions of lignin building blocks with their environment for the creation of additive models by means of quantum mechanical modelling. (Research)

Lignin has the potential to meet the growing need for sustainable and renewable aromatic building block in polymer chemistry. Its integration in polymer design is however hampered by several issues. The diversity in lignin structure and properties, due to the botanical source and extraction method (solvent choice) are the most important. As a result, lignin integration in materials design remains a time consuming trial-and-error task. An atomic scale digital twin can provide access to much needed understanding of the different types of lignin and their properties. The enLIGhtened project addresses the challenge of elucidating the structure & properties of lignin by creating a tandem approach using quantum chemical (QC) and artificial intelligence (AI) methods. At the QC level an environment dependent property library for the building blocks is created (unit level digital twin). The AI model, trained on QC data, provides access to full scale lignin model properties (full digital twin). A feedback loop between AI and QC accelerates their development, byfaster identification of relevant configurations (AI) with higher quality characterisation (QC) and fresh input for the AI model. Overall, enLIGhtened promises detailed fundamental theoretical insight in the structure, properties and behaviour of lignin. The resulting, multiscale predictive model will allow future polymer chemists a more rational and focussed lignin-based polymer synthesis design, to create sustainable products.

01 June 2024 - 31 May 2028