Data science techniques will be used in combination with quantum chemistry simulations to explore the otherwise intractable phase space resulting from gas phase molecules on catalyst surfaces to find relevant configurations and the lowest transition states between them. Image: Eric Hermes, Sandia National Laboratories
Chemical transformation technologies are present in virtually every sector, and their continued advancement requires a molecular-level understanding of underlying chemical processes. This project will facilitate and accelerate the quantitative description of crucial gas-phase and coupled heterogeneous catalyst/gas-phase chemical systems through the development of data-driven tools designed to revolutionize predictive catalysis and address DOE grand challenges.
Chemical transformation technologies are present in virtually every sector, and their continued advancement requires a molecular-level understanding of the underlying dynamic chemical processes. This is crucial to enable the development of new chemical processes for upgrading heavy fossil fuels, enabling the partial reduction of bio-derived feedstocks, and converting small molecules (such as CO, CO2 , and CH4) into larger and more valuable compounds. This project is developing a computational framework to accelerate discovery and characterization of these complex dynamic systems. Aurora will enable the researchers to explore systems relevant to the catalytic conversion of hydrocarbons, oxygenates, and small molecules .
In particular, this project aims to automate reaction path exploration on multidimensional potential energy surfaces using advanced machine learning algorithms coupled to quantum chemistry simulations suitable for exascale architectures; develop a complete computational infrastructure that generates reaction mechanisms for the target system; and create a modern, intelligent, user-friendly and accurate database that can be seamlessly updated. Such work promises to enable revolutionary advances in predictive catalysis.
