Overview:
Understanding atomic scale interactions of surfaces with reactants and reaction intermediates
under varied environmental conditions provides substantial insight into the activity,
durability, and selectivity of catalytic materials. This insight can serve as a valuable
guide for the synthesis of improved catalysts. Specifically, identifying key material
properties or "descriptors" that correlate with catalytic activity can be used to
accelerate materials discovery. To this end, ChemCatBio possesses the following core
capabilities:
- Periodic density functional theory calculations to determine binding energies, lateral
interaction energies, vibrational properties of adsorbates, activation energies, and
potential energy surfaces over a variety of pristine and defected catalyst surface
facets
- Comprehensive treatment of the local gas/liquid environment and expanded length and
time scales by leveraging density functional tight binding and accelerated molecular
dynamics approaches. These approaches allow us to span chemically relevant time scales
from picoseconds-microseconds, estimate reaction barriers in liquid phase, and extract
free energies and entropies, not merely energies of interaction. Further, these energetics
also allow us to formulate microkinetic models for complex fluids, such as bio-oil.
- Steady-state and dynamics flux modeling to guide strain (enzyme) development by predicting
the effect of modifying metabolic reactions as well as changing growth conditions.
These predictions are achieved by developing robust metabolic network models in-house,
leveraging public pathway and genome repositories such as BioCyc, MetaCyc, KEGG, and
JGI.
- Power law and micro-kinetic models to predict reaction conversion and selectivity
using a specific catalyst as a function of input conditions (e.g., temperature, pressure,
space, and time). The model can be parameterized based on thermodynamic and kinetic
data in the literature, first principles electronic structure calculations and/or
fitting to an experimental kinetic data set.
Additional Information:
Computational Pyrolysis Consortium
National Laboratory of the Rockies — Computational Modeling
National Laboratories:
Argonne National Laboratory
Los Alamos National Laboratory
National Laboratory of the Rockies
Pacific Northwest National Laboratory
