Machine learning for accelerated understanding of dynamic catalysis

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Does not meet definition

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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

Expected benefits

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.

System outputs

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

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported