Department of Energy program to advance computational electrochemistry


Accurate predictive simulations of the electrochemical reactions that power solar fuel generators, fuel cells, and batteries could advance these technologies through improved material design, and by preventing detrimental electrochemical processes, such as corrosion. However, electrochemical reactions are so complex that current computational tools can only model a fraction of all relevant factors at one time - with limited accuracy. This leaves researchers reliant on the trial and error of significant and expensive experimentation.

With the support of a $2.6 million grant from the Department of Energy, we will develop accurate, cost-effective, and highly accessible computational electrochemistry tools in collaboration with researchers at the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, the University of Colorado at Boulder, and the University of South Carolina. Specifically, we will combine electronic structure techniques beyond density-functional theory with classical models of electrolyte solvation at electrode surfaces, and apply these new combined methods on supercomputers to several electrochemical systems, creating an open database of these simulations. This will make it possible to use machine learning tools to enable accurate predictions for new materials even without performing expensive simulations.

See Rensselaer’s news coverage on this grant for further details.