Welcome to the Kitchin Group
Our group utilizes data science and machine learning to solve problems in catalysis and engineering. Our current focuses include developing machine learned potentials for molecular simulations, and design of experiments for high-throughput experimentation.
- September 24, 2023 New publication - Beyond Independent Error Assumptions in Large GNN Atomistic Models
- September 19, 2023 New publication - Sequential Sampling Methods for Finding Classification Boundaries in Engineering Applications
- April 25, 2023 New Publication - An Inverse Mapping Approach for Process Systems Engineering Using Automatic Differentiation and the Implicit Function Theorem
- April 16, 2023 New publication - WhereWulff A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions
- April 15, 2023 New publication - High throughput discovery of ternary Cu-Fe-Ru alloy catalysts for photo-driven hydrogen production
Current post (590 and counting)
In this work we show that prediction errors from graph neural networks for related atomistic systems tend to be correlated, and as a result the differences in energy are more accurate than the ...
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