SUNCAT Seed Funding Program


The SUNCAT-FWP has as its primary purpose the development of a predictive theory of heterogeneous catalysis through computational and experimental investigations. These theories are developed through extensive computational studies of trends in surface chemistry, which enable the identification of catalyst properties that are most important in determining the catalytic activity. We rely on detailed experimental investigations of catalysts and reactions to provide and test ideas. Ultimately the validity of our theories is evaluated through our ability to design new catalysts based on insight, something that is not possible without the combination of theory and experiment.

In particular, the FWP program focuses on developing systematic methods to map a large number of activation and reaction energies onto few descriptors, especially for new classes of catalysts such as 2D materials, ionic compounds, bi-functional catalysts, additives, and support effects. To determine electrochemical reaction energetics we are developing state-of-the-art methods to model the electrochemical interface. We have combined experimental-theoretical investigations of a large number of catalytic processes, including thermal syngas and methane activation reactions, electrochemical oxygen and hydrogen redox reactions. In addition we are developing theoretical methods and software infrastructure to support catalyst discovery, which includes machine learning tools for reaction network modeling and computational catalyst screening, a catalyst data warehouse, and kinetic modeling software. Essential components of experimental efforts include new synthesis methods for atomic scale control of surface composition and methodology for characterizing catalysts under realistic working conditions.


The JCAP program at SUNCAT focuses on the development of active and selective catalysts for electrochemical carbon dioxide reduction processes towards hydrocarbons and alcohols under mild temperatures and pressures. This process is an ideal method for to store the energy intermittent renewable sources such as wind and solar, in the form of energy dense liquid fuels. Theoretical and experimental efforts work in synergy by utilizing a theory-experiment feedback loop to aid in both mechanistic understanding and materials discovery for electrochemical CO2 reduction (CO2R).


The Toyota Research Institute (TRI) partnership with SUNCAT is aimed at (a) accelerating the discovery of materials for various applications using data-science and machine learning approaches (Thrust 1) and (b) discovering novel electrocatalysts for fuel cell applications (Thrust 2).

Thrust 1 PIs: Thomas Bligaard, Stefano Ermon
Thrust 2 (Theory) PIs: Jens Nørskov, Ambarish Kulkarni, Samira Siahrostami
2 (Experimental) PIs: Tom Jaramillo, Zhenan Bao, Yi Cui, Robert Sinclair