High-Throughput Screening of Metal Organic Frameworks for Zn-Ion Conductors Using Machine Learning Interatomic Potentials

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Metal organic frameworks (MOFs) are a relatively new class of materials with metal-complex based cations connected with organic linker chains. They offer a diverse range of properties due to combination of different cation complexes and linkers. Moreover, they contain pores which we can leverage for ion conduction. In this work, we explore the potential for Zn-ion conduction within the MOFs which can be utilized as electrodes or electrolytes in Zn-ion batteries using machine learning interatomic potentials (MLIPs). We benchmark several universal MLIP models—MACE [1], MatGL [2], ChgNet [3], and Fairchem [4]—by evaluating their ability to predict single-point energies and forces in MOFs. We apply fine-tuning for these uMLIPs with custom MOF datasets and utilize the models to find the Zn insertion sites and energy landscapes on a much larger ARCMOF dataset [5] with more than 280,000 MOFs. Further, we screen potential Zn-ion conductors by predicting the energy barriers and deriving ion migration pathways using the MLIP-predicted energy landscapes. This work provides a new avenue leveraging recent advancements in MLIPs to accelerate materials design for energy storage applications accurately. References: Batatia, I., Kovacs, D. P., Simm, G., Ortner, C., & Csányi, G. (2022). MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems, 35 , 11423–11436. Chen, C., & Ong, S. P. (2023). A universal graph deep learning interatomic potential for the periodic table. Nature Computational Science, 2 , 718–728. Deng, B., Zhong, P., Jun, K., et al. (2023). CHGNet: a pretrained universal neural network potential for charge-informed atomistic modelling. Nature Machine Intelligence, 5 , 1031–1041. Barroso-Luque, L., Shuaibi, M., Fu, X., Wood, B. M., Dzamba, M., Gao, M., Rizvi, A., Zitnick, C. L., & Ulissi, Z. W. (2024). Open Materials 2024 (OMat24) inorganic materials dataset and models. arXiv preprint arXiv:2410.12771 . Burner, J., Luo, J., White, A., Mirmiran, A., Kwon, O., Boyd, P. G., Maley, S., et al. (2023). ARC–MOF: A diverse database of metal-organic frameworks with DFT-derived partial atomic charges and descriptors for machine learning. Chemistry of Materials, 35 (3), 900–916.

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