Our research connects quantum chemistry methods and atomistic modeling with chemical and biorefinery process engineering to develop biobased materials for a circular economy. These research integrates density functional theory (DFT) calculations, molecular dynamics (MD) simulations, coarse grain (CG) modeling and machine learning (ML) algorithms, to design functional materials with tailored nanostructure that incorporate waste biomass, biomaterials and nanomaterials into the molecular designs. It implies fundamental research on the electronic structure, electron confinement, reactivity, and intermolecular interactions of several materials including: graphene, nanoparticles, lignin, nanocellulose, nanochitin, or protein-based biomaterials, among others (contact us for details).
We perform DFT electronic structure calculations of molecules and solids. We apply conceptual DFT reactivity descriptors (Fukui function, chemical hardness), as well as spin density and molecular electrostatic calculations to study reaction mechanisms.
We develop multiscale computational models for biomass-derived materials (nanocellulose, nanochitin, lignin).
We are starting to apply deep learning algorithms to predict chemical properties without solving quantum mechanics calculations
We apply Clar's sextet Theory to predict the aromaticity distribution of polycyclic aromatic systems: graphene, nanoribbons, asphaltenes. We apply these insights to design biobased nanostructured carbon materials
We study the polymerization and depolymerization mechanisms of lignin and other biomass materials, to provide fundamental knowledge that help the development of biorefineries
We design bioinspired materials for reuse as well as performance. We simulate reversible chemistry pathways to design materials for a circular economy