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The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions.
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Himanen, L.; Geurts, A.; Foster, A. S.; Rinke, P. Data-driven materials science: status, challenges, and perspectives. Advanced Science 2019
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Back, S.; Tran, K.; Ulissi, Z. W. Toward a design of active oxygen evolution catalysts: insights from automated density functional theory calculations and machine learning. Acs Catalysis 2019
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Rahali, S.; Ben Aissa, M. A.; Khezami, L.; Elamin, N.; Seydou, M.; Modwi, A. Adsorption behavior of Congo red onto barium-doped ZnO nanoparticles: correlation between experimental results and DFT calculations. Langmuir 2021
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Andersen, M.; Levchenko, S. V.; Scheffler, M.; Reuter, K. Beyond scaling relations for the description of catalytic materials. ACS Catalysis 2019
2019
Cited alongside, same era.
Back, S.; Yoon, J.; Tian, N.; Zhong, W.; Tran, K.; Ulissi, Z. W. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. The journal of physical chemistry letters 2019
2019
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Mamun, O.; Winther, K. T.; Boes, J. R.; Bligaard, T. High-throughput calculations of catalytic properties of bimetallic alloy surfaces. Scientific data 2019
2019
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del Río, E. G.; Mortensen, J. J.; Jacobsen, K. W. Local Bayesian optimizer for atomic structures. Physical Review B 2019
2019
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García-Muelas, R.; López, N. Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals. Nature communications 2019
2019
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Jinnouchi, R.; Lahnsteiner, J.; Karsai, F.; Kresse, G.; Bokdam, M. Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on the fly with Bayesian inference. Physical review letters 2019
2019
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Torres, J. A. G.; Jennings, P. C.; Hansen, M. H.; Boes, J. R.; Bligaard, T. Low-scaling algorithm for nudged elastic band calculations using a surrogate machine learning model. Physical review letters 2019
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2019
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2021
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2021
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Sriram, A.; Das, A.; Wood, B. M.; Zitnick, C. L. Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations. International Conference on Learning Representations. 2021
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Ying, C.; Cai, T.; Luo, S.; Zheng, S.; Ke, G.; He, D.; Shen, Y.; Liu, T.-Y. Do Transformers Really Perform Badly for Graph Representation? Advances in Neural Information Processing Systems 2021
2021
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Godwin, J.; Schaarschmidt, M.; Gaunt, A. L.; Sanchez-Gonzalez, A.; Rubanova, Y.; Veličković, P.; Kirkpatrick, J.; Battaglia, P. Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. International Conference on Learning Representations. 2021
2021
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Open Catalyst Project Challenge. https://opencatalystproject.org/challenge.html , 2021
2021
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Chen, C.; Ong, S. P. AtomSets as a hierarchical transfer learning framework for small and large materials datasets. npj Computational Materials 2021
2021
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IS2RE Leaderboard Concerns. https://discuss.opencatalystproject.org/t/is2re-leaderboard-concerns/66 , 2021
2021
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2021
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Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A., et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021
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Busk, J.; Jørgensen, P. B.; Bhowmik, A.; Schmidt, M. N.; Winther, O.; Vegge, T. Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks. Machine Learning: Science and Technology 2021
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Guan, Y.; Chaffart, D.; Liu, G.; Tan, Z.; Zhang, D.; Wang, Y.; Li, J.; Ricardez-Sandoval, L. Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives. Chemical Engineering Science 2022
2022
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Rosen, A. S.; Notestein, J. M.; Snurr, R. Q. Realizing the data-driven, computational discovery of metal-organic framework catalysts. Current Opinion in Chemical Engineering 2022
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Toniato, A.; Vaucher, A. C.; Laino, T. Grand challenges on accelerating discovery in catalysis. Catalysis Today 2022
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Kolluru, A.; Shoghi, N.; Shuaibi, M.; Goyal, S.; Das, A.; Zitnick, L.; Ulissi, Z. W. Transfer Learning using Attentions across Atomic Systems with Graph Neural Networks (TAAG). The Journal of Chemical Physics 2022
2022
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