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.

News

• 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
• January 01, 2023 2022 in a nutshell
• September 29, 2022 New publication - Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

Current post (584 and counting)

New Publication - An Inverse Mapping Approach for Process Systems Engineering Using Automatic Differentiation and the Implicit Function Theorem April 25, 2023

Solving inverse problems, where we know what outputs we want from a model and seek the inputs that provide them, is a difficult task. A conventional approach to this problem is to use a nonline ... click here for more