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
- February 11, 2025 New publication - Accessing Numerical Energy Hessians With Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis
- February 05, 2025 New publication - Beyond the Fourth Paradigm of Modeling in Chemical Engineering
- February 04, 2025 New publication - Integrated systems-To-Atoms (S2A) Framework for Designing Resilient and Efficient Hydrogen Infrastructure Solutions
- February 03, 2025 New publication - Multiscale Optimization of Formic Acid Dehydrogenation Process via Linear Model Decision Tree Surrogates
- February 02, 2025 New publication - Enumeration of surface site nuclearity and shape in a database of intermetallic low-index surface facets
Current post (611 and counting)
New publication - Accessing Numerical Energy Hessians With Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis February 11, 2025
The ability to calculate energy Hessians has long been a cornerstone of understanding chemical reactions, but traditional methods like density functional theory (DFT) are computationally expens ...
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