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Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy.
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Langley, P · 2000
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Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 2003
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Klicpera, J., Giri, S., Margraf, J. T., and Günnemann, S · 2011
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. and Grossman, J. C · 2018
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Graph networks as a universal machine learning framework for molecules and crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C., and Ong, S. P · 2019
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Progress in accurate chemical kinetic modeling, simulations, and parameter estimation for heterogeneous catalysis
Matera, S., Schneider, W. F., Heyden, A., and Savara, A · 2019
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Evalai: Towards better evaluation systems for ai agents
Yadav, D., Jain, R., Agrawal, H., Chattopadhyay, P., Singh, T., Jain, A., Singh, S. B., Lee, S., and Batra, D · 2019
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SE(3)-transformers: 3D roto-translation equivariant attention networks
Fuchs, F. B., Worrall, D. E., Fischer, V., and Welling, M · 2020
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SE(3)-transformers: 3D roto-translation equivariant attention networks
Fuchs, F. B., Worrall, D. E., Fischer, V., and Welling, M · 2020
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Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J., and Dror, R · 2020
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Global energy outlook 2020: energy transition or energy addition
Newell, R., Raimi, D., Villanueva, S., Prest, B., et al · 2020
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Learning mesh-based simulation with graph networks
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W · 2020
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Open catalyst 2020 (oc20) dataset and community challenges
Chanussot, L., Das, A., Goyal, S., Lavril, T., Shuaibi, M., Riviere, M., Tran, K., Heras-Domingo, J., Ho, C., Hu, W., et al · 2021
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Simple gnn regularisation for 3d molecular property prediction & beyond
Godwin, J., Schaarschmidt, M., Gaunt, A., Sanchez-Gonzalez, A., Rubanova, Y., Veličković, P., Kirkpatrick, J., and Battaglia, P · 2021
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Accelerated prediction of cu-based single-atom alloy catalysts for co2 reduction by machine learning
Wang, D., Cao, R., Hao, S., Liang, C., Chen, G., Chen, P., Li, Y., and Zou, X · 2021
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Do Transformers Really Perform Bad for Graph Representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T. Y · 2021
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2022
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How do graph networks generalize to large and diverse molecular systems?
Gasteiger, J., Shuaibi, M., Sriram, A., Günnemann, S., Ulissi, Z., Zitnick, C. L., and Das, A · 2022
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Liang, C., Wang, B., Hao, S., Chen, G., Heng, P.-A., and Zou, X · 2022
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D · 2021
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Gemnet: Universal directional graph neural networks for molecules
Klicpera, J., Becker, F., and Günnemann, S · 2021
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A geometric deep learning approach to predict binding conformations of bioactive molecules
Méndez-Lucio, O., Ahmad, M., del Rio-Chanona, E. A., and Wegner, J. K · 2021
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A geometric deep learning approach to predict binding conformations of bioactive molecules
Méndez-Lucio, O., Ahmad, M., del Rio-Chanona, E. A., and Wegner, J. K · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
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Neural message passing for quantum chemistry
Gilmer, J. et al
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Neural message passing for quantum chemistry
Gilmer, J. et al
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Liao, Y.-L. and Smidt, T · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Shi, Y., Zheng, S., Ke, G., Shen, Y., You, J., He, J., Luo, S., Liu, C., He, D., and Liu, T.-Y · 2022
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Towards training billion parameter graph neural networks for atomic simulations
Sriram, A., Das, A., Wood, B. M., Goyal, S., and Zitnick, C. L · 2022
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2022
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Spherical channels for modeling atomic interactions
Zitnick, C. L., Das, A., Kolluru, A., Lan, J., Shuaibi, M., Sriram, A., Ulissi, Z., and Wood, B · 2022
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