Scaling relations in single-site catalysis

Computational catalyst screening is limited primarily by the efficiency with which accurate predictions can be made. In bulk heterogeneous catalysis, linear free energy relationships (LFERs) accelerate screening by relating catalytic activity back to the adsorption energies of key intermediates, but their applicability to single-site catalysts remains unclear, in view of the directional, covalent metal-ligand bonds and the broader chemical space of accessible ligand scaffolds.

Recovering exact conditions in semilocal DFT

Widely employed semi-local DFT suffers from well-known errors that prevent its robust predictio, e.g. in materials and catalyst design. This failure in semi-local DFT can be traced to the violation of exchange-correlation approximations of key exact conditions. The flat-plane condition is the union of two exact constraints in electronic structure theory: (i) energetic piecewise linearity with fractional electron removal or addition and (ii) invariant energetics with change in electron spin in a half filled orbital.

Inorganic features for machine learning

Machine learning models can accelerate the discovery of new materials by allowing rapid screening of potential candidates, but depend on the way the molecule is represented. For transition metal complexes, accurate calculations are computationally costly and available training data sets are small, so finding the correct representation becomes a critical ingredient for model predictive accuracy.

Unifying sensitivity in catalysis and spin states

Open-shell single-site transition metal (TM) complexes are efficient catalysts, but challenging to screen computationally because of 1) the need to simultaneously predict spin-splitting energies, activation barriers and reaction energies, and 2) well-known challenges of approximate DFT for these predictions that manifest as strong sensitivity to the amount of exact exchange in the functional. Hence, advancing DFT-based screening requires understanding how computed energetics are sensitive to exact exchange.


About Us

The Kulik group focuses on the development and application of new electronic structure methods and atomistic simulations tools in the broad area of catalysis.

Our Interests

We are interested in transition metal chemistry, with applications from biological systems (i.e. enzymes) to nonbiological applications in surface science and molecular catalysis.

Our Focus

A key focus of our group is to understand mechanistic features of complex catalysts and to facilitate and develop tools for computationally driven design.

Contact Us

Questions or comments? Let us know! Contact Dr. Kulik: