QuaTOMs

The properties of high-tech materials are governed by their atomic scale structure, with defects playing a central role. At QuATOMs, we investigate materials properties through quantum mechanical calculations, focusing on materials for quantum technological and energy applications.

Lignin presents a sustainable renewable bio-based alternative for the polymer industry’s current oil-based aromatic feedstock. Within the QuATOMs group, our research focusses on elucidating the complex molecular structure of lignin and discovering global structure-property relations, making use of quantum chemical calculations and AI-based models.  

The use of Machine Learning and Artificial Intelligence in research is limited by the need for large datasets. At QuATOMs, we develop dedicated models directed at small datasets of <50 samples for real-life lab applications.

The BioEconomy project DigiLignin addresses the challenges of lignin-based polyurethane design by creating a model that could predict the structure-property relationships via a combined Machine Learning (ML) and quantum mechanical (QM) modelling approach.

The QuantumLignin project aims to elucidate the structure-property relationships of lignin as function of the chemical environment, creating an additive model of lignin.

Within this project we aim to accelerate the quantum mechanical prediction of vibrational spectra allowing for their use in large scale complex systems.

This bilateral project with UNamur is directed at reducing the computational cost of accurate electronic structure calculations for complex defect systems.

The GIMLi project strives to link different lignin structures to specific properties by making use of machine learning models with a focus on sustainability.

This joint QuATOMs–HyMaD project aims to establish the structure–property relationships that govern the chiroptical and optoelectronic properties of chiral hybrid perovskites, enabling the rational design of these materials for next-generation chiroptoelectronic devices.