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
- December 10, 2023 New publication - Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set
- December 03, 2023 New publication - Chemical Properties from Graph Neural Network-Predicted Electron Densities
- 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
Current post (592 and counting)
New publication - Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set December 10, 2023
In this work, we show that we can use large graph neural networks to predict transition metal complex energies. We developed an improved dataset at a higher level of theory, and tested models r ...
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