The Kitchin Research Group

Geometry optimization is an expensive part of DFT; each step requires a DFT step. The Open Catalyst Project provides pre-trained machine learned potentials that provide cheap forces for a broad range of metallic, intermetallic materials. In this work we use models trained on the OC20 dataset to accelerate geometry optimization of materials outside that domain including larger adsorbates, oxides, and zeolites. With fine-tuning, we are able to reduce the number of DFT calls required substantially for these systems.

@article{10.1088/2632-2153/ad37f0,
        author={Wang, Xiaoxiao and Musielewicz, Joseph and Tran, Richard and Ethirajan, Sudheesh Kumar and Fu, Xiaoyan and Mera, Hilda and Kitchin, John R and Kurchin, Rachel and Ulissi, Zachary W},
        title={Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials},
        journal={Machine Learning: Science and Technology},
        url={http://iopscience.iop.org/article/10.1088/2632-2153/ad37f0},
        year={2024}
}

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