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 17, 2024 New publication - Circumventing data imbalance in magnetic ground state data for magnetic moment predictions
- 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
Current post (593 and counting)
New publication - Circumventing data imbalance in magnetic ground state data for magnetic moment predictions February 17, 2024
Modeling magnetic materials with DFT is hard. In this work we develop a machine learning approach to predicting magnetic properties of materials based on their structure. Our two stage model fi ...
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