Computational Modeling

NREL uses computational modeling to increase the efficiency of biomass conversion by rational design using multiscale modeling, applying theoretical approaches, and testing scientific hypotheses.

model of enzymes wrapping on cellulose; colorful circular structures entwined through blue strands

Cellulosomes are complexes of protein scaffolds and enzymes that are highly effective in decomposing biomass. This is a snapshot of a coarse-grain model of complex cellulosome binding to a coarse grain model of cellulose fibrils. The complex cellulosome is constructed of seven cellulosomes (green, blue, cyan, pink, purple, majenta, and yellow) bound to a secondary scaffold (red) by cohesin-dockerin pairs. The secondary scaffold is very flexible and allows for motion of the primary cellulosomes within a range. The primary cellulosomes are made of nine enzymes each bound to a primary scaffold via cohesin-dockerin pairing with many flexible linkers. All primary scaffolds and some enzymes have cellulose binding domains for attaching to cellulose.

Featured Publications

Illustration showing identified minimum-energy pathway (solid curves) of H reduction of the ideal NiO(100) surface. State 1, the initial state, is the ideal surface with two H2 molecules adsorbed on two Ni sites. In state 2, one of the two adsorbed H2 molecules dissociates and an H-H pair is formed. The atomic structure of state 3 is shown in the lower inset indicated by an arrow from the number 3. The dashed curve shows a direct reaction from 1 to 3 without pre-dissociation of the first H2 molecule. The structure and partial charge density (at the highest occupied band) of the transition state for the 1 to 3 reaction step are shown in the upper inset.

Initial reduction of the NiO(100) surface in hydrogen, The Journal of Chemical Physics (2013)


View all NREL bioenergy computational modeling publications.


Illustration of a carbohydrate binding domain (white cartoon with colored licorice-shaped sugars decorating it) on a cellulose surface (depicted as green solvent-accessible surface

Computer Enzyme Design

NREL's molecular modeling provides insight into enzyme mechanisms and enables higher performance and new functionality for industrial enzymes. For example, the study depicted by the illustration to the left determined the structure and binding orientation of several glycosylation patterns on the binding domain, which we showed in previous studies to greatly enhance the binding to cellulose, a property that has been shown to improve the enzyme activity in breaking down cellulose in biomass.

Structure of cellulose nanofibrils shown as sticks, showing side view with layers easily seen (top), top view where the sugar rings are shown (middle), and the twisting behavior, untwisted bottom left, and with natural twist bottom right.

Cell Wall Components

We use multi-scale modeling to investigate the properties of cellulose, hemicellulose, lignin, and pectin. Together they compose cell walls and are the source of biofuels and biomaterials. Our modeling investigates their properties for better conversion and material design.

Illustration of core carbon metabolic model of Actinobacillus succinogenes that shows fluxes between central metabolic nodes. Production of succinate is limited by the redox balance of the cell, in which NADH consuming reactions (orange) must be balanced with NADH producing reactions (green).

Metabolic Modeling

To increase yields and redirect production to desired products, we model the existing pathways of microbial conversion of sugars to fuels and high-value products.

Illustration showing how catalysts are designed to convert biomass to liquid fuels and chemicals. On the left is a stylized tree/forest labeled "Biomass"; partial hexagons and hexagonal lines are to the right with an arrow pointing right and labeled "Zeolites"; the arrow points to "Furans," a pentagon shape with four bars jutting out; a right arrow then points to stylized icons of "Plastic Resins," "Diesel," and "Gasoline" trucks and pumps.

Quantum Mechanical Models

NREL studies chemical and electronic properties and processes to reduce barriers in conversion and upgrading of renewable sources.

Illustration of a computational tool for computing multi-phase adsorption on catalytic surfaces.

Catalyst Science and Design

We use ab initio reaction energetics coupled with experimental kinetic models to predict catalyst performance under realistic reaction conditions.

microscopic photos of poplar and pine particles and their respective 3D models

Biomass Mesostructure and Continuum Multiphysics Modeling

We are developing realistic, species-specific particle models that predict how feedstocks and blendstocks will perform in various conversion processes.

Illustration of a reactor simulation of a bubbling catalyst reactor: a three-dimensional cylinder displaying infrared temperatures in blue, green, yellow, orange, and red, with red and orange block-like structures forming a circle and coming out of the top of the cylinder.

Reactor Simulations

We use multiphase (solid, liquid, and/or s + gas) flow simulations to describe, understand, and optimize reactors used in catalytic pyrolysis, enzymatic hydrolysis, and fermentation. Efforts also include coupling flow models with reaction kinetics and interphase transport. experiments.

Research Team

Principal Investigators

Seonah Kim

Seonah Kim

Senior Scientist, Computational Catalyst Design R&D
Peter Ciesielski

Peter Ciesielski

Research Scientist, Bioenergy and Biomaterials

Lintao Bu

Thomas Foust

Brandon Knott

James Lischeske

Ambarish Nag

Mark Nimlos

Hariswaran Sitaraman

Mike Sprague

Related and Integrated Programs

Biological and Catalytic Conversion of Sugars and Lignin

Biomass Characterization

Biomass Deconstruction and Pretreatment

Biomass Feedstocks

Enzyme and Microbial Development

Heterogeneous Catalysis for Thermochemical Conversion

Strategic and Market Analysis

Thermochemical Process Integration, Scale-Up, and Piloting


BioEnergy Science Center

C3Bio: Center for Direct Catalytic Conversion of Biomass to Biofuels

Computational Pyrolysis Consortium