Skip to main content


Computational catalyst design with industrial application 

Theory and computation are most powerful when making experimental predictions. My group collaborates with industrial companies, such as Chevron Phillips Chemical and Phillips 66, to computationally design new catalysts that are then experimentally realized. 

Over the past several years we have designed new, selective ethylene oligomerization catalysts using transition-state theory and machine learning methods.

  • “Computational Transition-State Design Provides Experimentally Verified Cr(P,N) Catalysts for Control of Ethylene Trimerization and Tetramerization” ACS Catal. 20188, 1138-1142.
  • “Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization” Chem. Sci. 2020, 9665-9674.

Read More:

Organometallic reaction dynamics

Metal-mediated organometallic reaction mechanisms are assumed to be appropriately described by minimum energy pathways mapped out by density functional theory (DFT) calculations. Using our program Milo for quasiclassical dynamics trajectories, we have shown that the classical demarcation between concerted and two-step mechanisms need to be revisited. See recent publications:

  • “Experiments and Direct Dynamics Simulations Reveal a Network of Reaction Pathways for Tungsten h2-Arene - Aryl Hydride Equilibria” J. Am. Chem. Soc. 2020Just Accepted
  • “Dynamical Mechanism May Avoid High-Oxidation State Ir(V)-H Intermediate and Coordination Complex in Alkane and Arene C-H Activation by Cationic Ir(III) Phosphine” J. Am. Chem. Soc. 2018140, 11039-11045.

Computational studies of alkane C-H functionalization

Large quantities of light alkanes from natural gas are readily available. A grand goal is the conversion of methane and other light alkanes into liquid alcohols. Our long-term goal is to use computational chemistry tools to develop general principles on mechanisms, intermediates, reactivity, and selectivity for hydrocarbon C-H functionalization reactions by p-block main-group and transition metal complexes. We also predict new alkane C-H functionalization catalysts that can be experimentally verified. See recent publications:

  • Selective C-H Functionalization of Methane and Ethane by a Molecular Sb(V) Complex” Angew. Chem. Int. Ed. 201958, 2241-2245.
  • “Electrophilic Impact of High-Oxidation State Main-Group Metal and Ligands on Methane C-H Activation and Functionalization Reactions” Organometallics2018, 37, 3045-3054.
  • “Catalytic Mechanism and Efficiency of Methane Oxidation by Hg(II) in Sulfuric Acid and Comparison to Radical Initiated Conditions” ACS Catal. 20166, 4312-4322.

Machine learning analysis of direct dynamics trajectories

While reaction trajectories model and rationalize dynamical reaction effects, understanding and predicting the outcome of deterministic trajectories based on initial conditions is nontrivial due to highly complex multi-dimensional energy landscapes. We have pioneered the use of machine learning to provide quantitative analysis and develop predictive models for trajectory outcomes. This provides the ability for qualitative chemical explanations to emerge from machine learning analysis. See the recent publication:

  • “Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene” J. Phys. Chem. A 2020124, 4813-4826.