Machine learning for accelerated understanding of dynamic catalysis
Description
The understanding of catalytic reactions has been a long-standing challenge due to the complexity and wide range of time scales involved in their mechanisms. There remain significant gaps in the understanding of how the catalyst's atomistic structure
Detailed example
The understanding of catalytic reactions has been a long-standing challenge due to the complexity and wide range of time scales involved in their mechanisms. There remain significant gaps in the understanding of how the catalyst's atomistic structure determines the activity of reactions and how it is transiently transformed under varying operating conditions. The proposed effort seeks to take on this challenge with a data-science-driven approach to computational modeling, joining it with advanced experimental methods of characterization to create new methods for capturing realistic complexity of reactions at heterogenous and disordered interfaces. As a prototype application, we will focus on the water gas shift reaction (WGSR) CO + H2O → CO2 +H2, as carried out over an active oxide (ceria CeO2) supported nanoscale Pt cluster catalyst. The Pt/CeO2 system is a high activity, low temperature catalyst for WGSR in which transient catalyst reconstructions and fluxional oscillating behavior
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The understanding of catalytic reactions has been a long-standing challenge due to the complexity and wide range of time scales involved in their mechanisms. There remain significant gaps in the understanding of how the catalyst's atomistic structure determines the activity of reactions and how it is transiently transformed under varying operating conditions. The proposed effort seeks to take on this challenge with a data-science-driven approach to computational modeling, joining it with advanced experimental methods of characterization to create new methods for capturing realistic complexity of reactions at heterogenous and disordered interfaces. As a prototype application, we will focus on the water gas shift reaction (WGSR) CO + H2O → CO2 +H2, as carried out over an active oxide (ceria CeO2) supported nanoscale Pt cluster catalyst. The Pt/CeO2 system is a high activity, low temperature catalyst for WGSR in which transient catalyst reconstructions and fluxional oscillating behavior of active sites at the metal-support interface play an essential role.
Audit / financial statement impact
Does not meet definition
Controls / human review
ATO: Not reported; PIA: Not published