Computational Modeling and Simulations

We apply these capabilities, including machine learning and artificial intelligence, to fundamental scientific questions, such as elucidating the molecular-scale structure/property/function relationships in plant tissues, bio-based materials, and enzymes. They also support applied projects with industry partners, such as reactor modeling process scale-up and optimization.

Our researchers work closely with experimental teams from academia, industry, and other national labs to develop, validate, and apply computational tools to deliver impactful solutions to the unique challenges associated with bioenergy and catalytic processes and adding value to waste materials.

Key Capabilities

Key computational modeling and simulation capabilities include:

• Density functional theory for modeling reactive processes in biological and chemical catalytic systems
• Molecular dynamics, with specific expertise in (bio)-polymers and their natural and synthetic composites
• Mesoscale modeling of reactive transport processes in nano- and micro-structured systems, including biomass particles, formed catalysts, and structured electrodes
• Direct import of 3D image data (e.g., X-ray and electron tomography) for detailed and accurate representation of system geometry
• Kinetic model development and computational extraction of kinetic parameters from experimental data
• Computational fluid dynamics (CFD) simulations for process scaleup and reactor design and optimization
• Machine learning and artificial intelligence, with a focus on applying these methods for functional protein and enzyme design, multiscale coupling, and reduced-order model development.