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We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture.
1903
Earlier work this paper cites.
1903
Earlier work this paper cites.
1907
Earlier work this paper cites.
Caruana, R.: Multitask learning: a knowledge-based source of inductive bias. Mach. Learn. 48
1993
Earlier work this paper cites.
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation and active learning. In: Proceedings of the 7th International Conference on Neural Information Processing Systems. NIPS’94, pp. 231–238. MIT Press, Cambridge, MA, USA (1994). https://proceedings.neurips.cc/paper_files/paper/1994/file/b8c37e33defde51cf91e1e03e51657da-Paper.pdf
1994
Earlier work this paper cites.
Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. Journal of Global Optimization 13
1998
Earlier work this paper cites.
Maguire, J., Benedict, M., Woodcock, L., LeClair, S.: Artificial intelligence in materials science: Application to molecular and particulate simulations. MRS Commun. 700
2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51
2008
Earlier work this paper cites.
Balabin, R.M., Lomakina, I.: Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies. J. Chem. Phys. 131
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 2546–2554. Curran Associates, Inc., Red Hook, NY, USA (2011). https://proceedings.neurips.cc/paper_files/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf
2011
Earlier work this paper cites.
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M.a., Senior, A., Tucker, P., Yang, K., Le, Q., Ng, A.: Large scale distributed deep networks. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1223–1231. Curran Associates, Inc., Red Hook, NY, USA (2012). https://proceedings.neurips.cc/paper_files/paper/2012/file/6aca97005c68f1206823815f66102863-Paper.pdf
2012
Earlier work this paper cites.
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 2960–2968. Curran Associates, Inc., Red Hook, NY, USA (2012). https://proceedings.neurips.cc/paper_files/paper/2012/file/05311655a15b75fab86956663e1819cd-Paper.pdf
2012
Earlier work this paper cites.
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Parallel algorithm configuration. In: Learning and Intelligent Optimization (LION), pp. 55–70 (2012)
2012
Earlier work this paper cites.
Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., Persson, K.A.: Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials 1
2013
Earlier work this paper cites.
Swersky, K., Snoek, J., Adams, R.P.: Multi-task Bayesian optimization. In: Advances in Neural Information Processing Systems (NIPS), pp. 2004–2012 (2013). https://proceedings.neurips.cc/paper_files/paper/2013/file/f33ba15effa5c10e873bf3842afb46a6-Paper.pdf
2013
Earlier work this paper cites.
Li, M., Andersen, D.G., Park, J.W., Smola, A.J., Ahmed, A., Josifovski, V., Long, J., Shekita, E.J., Su, B.-Y.: Scaling distributed machine learning with the parameter server. In: Proceedings of the 11th USENIX Conference on Operating Systems Design and Implementation. OSDI’14, pp. 583–598. USENIX Association, USA (2014). https://dl.acm.org/doi/10.5555/2685048.2685095
2014
Earlier work this paper cites.
Snoek, J.: Bayesian optimization and semiparametric models with applications to assistive technology. University of Toronto, Canada (Ph.D. Thesis) (2014)
2014
Earlier work this paper cites.
Rabenstein, B., Volz, J.: Prometheus: A Next-Generation Monitoring System (Talk). USENIX Association, Dublin (2015)
2015
Earlier work this paper cites.
Gonzalez, J., Dai, Z., Hennig, P., Lawrence, N.D.: Batch Bayesian optimization via local penalization. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 648–657 (2016). http://proceedings.mlr.press/v51/gonzalez16a.pdf
2016
Earlier work this paper cites.
Brockherde, F., Vogt, L., Tuckerman, M.E., Burke, K., Müller, K..R.: Bypassing the Kohn-Sham equations with machine learning. Nat. Commun. 8
2017
Earlier work this paper cites.
Wang, C., Tharval, A., Kitchin, J.R.: A density functional theory parameterised neural network model of zirconia. Mol. Simul. 44
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., ??? (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Schütt, K.T., Kindermans, P.-J., Sauceda, H.E., Chmiela, S., Tkatchenko, A., Müller, K.-R.: SchNet: a continuous-filter convolutional neural network for modeling quantum interactions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 992–1002. Curran Associates Inc., Red Hook, NY, USA (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/303ed4c69846ab36c2904d3ba8573050-Paper.pdf
2017
Earlier work this paper cites.
Balaprakash, P., Salim, M., Uram, T.D., Vishwanath, V., Wild, S.M.: Deephyper: Asynchronous hyperparameter search for deep neural networks. In: 2018 IEEE 25th International Conference on High Performance Computing (HiPC), pp. 42–51 (2018). https://doi.org/10.1109/HiPC.2018.00014
2018
Earlier work this paper cites.
Xie, T., Grossman, J.C.: Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120
2018
Earlier work this paper cites.
Frazier, P.I.: Bayesian optimization. In: INFORMS TutORials in Operations Research, pp. 255–278 (2018). https://doi.org/10.1287/educ.2018.0188 . https://doi.org/10.1287/educ.2018.0188
2018
Earlier work this paper cites.
Chandrasekaran, A., Deepak, K., Batra, E., Kim, C., Chen, L., Ramprasad, R.: Solving the electronic structure problem with machine learning. NPJ Comput. Mater. 5
2019
Earlier work this paper cites.
Custódio, C.A., Filletti, E.R., ca, V.V.F.: Artificial neural networks for density-functional optimizations in fermionic systems. Sci. Rep. 9
2019
Cited alongside, same era.
Schleder, G.R., Padhila, A.C.M., Acosta, C.M., Costa, M., Fazzio, A.: From DFT to machine learning: recent approaches to materials science–a review. JPhys. Materials 2
2019
Cited alongside, same era.
Vasudevan, R.K., Choudhary, K., Mehta, A., Smith, R., Kusne, G., Tavazza, F., Vlcek, L., Ziatdinov, M., Kalinin, S.V., Hattrick-Simpers, J.: Materials science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics. MRS Commun. 9
2019
Cited alongside, same era.
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019). https://api.semanticscholar.org/CorpusID:52967399
Balin, M.F., Sancak, K., Catalyurek, U.V.: MG-GCN: A Scalable Multi-GPU GCN Training Framework. In: Proceedings of the 51st International Conference on Parallel Processing. ICPP ’22. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3545008.3545082 . https://doi.org/10.1145/3545008.3545082
2022
Later among the works it cites.
2022
Later among the works it cites.
Lupo Pasini, M., Zhang, P., Reeve, S.T., Choi, J.Y.: Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems. Mach. learn.: sci. technol. 3
2022
Later among the works it cites.
Choi, J.Y., Zhang, P., Mehta, K., A., B., Lupo Pasini, M.: Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. J Cheminform 14
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2019
Cited alongside, same era.
Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K.A., Ceder, G., Jain, A.: MatBERT: A Materials Domain Language Model for Text Mining and Information Extraction. Nature 571
2019
Cited alongside, same era.
Ryu, S., Kwon, Y., Kim, W.Y.: A bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification. Chemical science 10
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc., Red Hook, NY, USA (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: an imperative style, high-performance deep learning library, pp. 8026–8037. Curran Associates Inc., Red Hook, NY, USA (2019). https://dl.acm.org/doi/pdf/10.5555/3454287.3455008
2019
Cited alongside, same era.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21
2020
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901. Curran Associates, Inc., Red Hook, NY, USA (2020). https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Hirschfeld, L., Swanson, K., Yang, K., Barzilay, R., Coley, C.W.: Uncertainty quantification using neural networks for molecular property prediction. Journal of Chemical Information and Modeling 60
2020
Cited alongside, same era.
2022
Later among the works it cites.
Egele, R., Maulik, R., Raghavan, K., Lusch, B., Guyon, I., Balaprakash, P.: AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp. 1908–1914. IEEE Computer Society, Los Alamitos, CA, USA (2022). https://doi.org/10.1109/ICPR56361.2022.9956231 . https://doi.ieeecomputersociety.org/10.1109/ICPR56361.2022.9956231
2022
Later among the works it cites.
Yoo, P., Bhowmik, D., Mehta, K., Zhang, P., Liu, F., Lupo Pasini, M., Irle, S.: Deep learning workflow for the inverse design of molecules with specific optoelectronic properties. Scientific Reports 13
2023
Later among the works it cites.
Kuenneth, C., Ramprasad, R.: polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics. Nature Communications 14
2023
Later among the works it cites.
Takeda, S., Priyadarsini, I., Kishimoto, A., Shinohara, H., Hamada, L., Masataka, H., Fuchiwaki, J., Nakano, D.: Multi-modal foundation model for material design. In: AI for Accelerated Materials Design - NeurIPS 2023 Workshop (2023). https://openreview.net/forum?id=EiT2bLsfM9
2023
Later among the works it cites.
Lee, K.L.K., Gonzales, C., Spellings, M., Galkin, M., Miret, S., Kumar, N.: Towards foundation models for materials science: The open matsci ml toolkit. In: Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. SC-W ’23, pp. 51–59. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3624062.3626081 . https://doi.org/10.1145/3624062.3626081
2023
Later among the works it cites.
Lupo Pasini, M., Choi, J.Y., Zhang, P., Baker, J.: User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks (2023) https://doi.org/10.2172/2224153
2023
Later among the works it cites.
Falk, J., Bonati, L., Novelli, P., Parrinello, M., Pontil, M.: Transfer learning for atomistic simulations using gnns and kernel mean embeddings. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems, vol. 36, pp. 29783–29797. Curran Associates, Inc., Red Hook, NY, USA (2023). https://proceedings.neurips.cc/paper_files/paper/2023/file/5f02c76bc411a6f7c9a8bb2cbf981260-Paper-Conference.pdf
2023
Later among the works it cites.
Tran, R., Lan, J., Shuaibi, M., Wood, B.M., Goyal, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., Sriram, A., Therrien, F., Abed, J., Voznyy, O., Sargent, E.H., Ulissi, Z., Zitnick, C.L.: The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts. ACS Catalysis 13
2023
Later among the works it cites.
Huang, K., Jin, Y., Candès, E., Leskovec, J.: Uncertainty quantification over graph with conformalized graph neural networks. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. NIPS ’23, vol. 36. Curran Associates Inc., Red Hook, NY, USA (2024). https://proceedings.neurips.cc/paper_files/paper/2023/file/54a1495b06c4ee2f07184afb9a37abda-Paper-Conference.pdf
2023
Later among the works it cites.
2023
Later among the works it cites.
Miret, S., Lee, K.L.K., Gonzales, C., Nassar, M., Spellings, M.: The open MatSci ML toolkit: A flexible framework for machine learning in materials science. Transactions on Machine Learning Research (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Baker, J., Lupo Pasini, M., Hauck, C.: Invariant features for accurate predictions of quantum chemical uv-vis spectra of organic molecules. ChemRxiv (2023) https://doi.org/10.26434/chemrxiv-2023-9n306 . This content is a preprint and has not been peer-reviewed
2023
Later among the works it cites.
Choi, J.Y., Lupo Pasini, M., Zhang, P., Mehta, K., Liu, F., Bae, J., Ibrahim, K.: Ddstore: Distributed data store for scalable training of graph neural networks on large atomistic modeling datasets. In: Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. SC-W ’23, pp. 941–950. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3624062.3624171 . https://doi.org/10.1145/3624062.3624171
2023
Later among the works it cites.
Kadra, A., Janowski, M., Wistuba, M., Grabocka, J.: Scaling laws for hyperparameter optimization. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. NIPS ’23. Curran Associates Inc., Red Hook, NY, USA (2024). https://proceedings.neurips.cc/paper_files/paper/2023/file/945c781d7194ea81026148838af95af7-Paper-Conference.pdf
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
Beaini, D., Huang, S., Cunha, J.A., Li, Z., Moisescu-Pareja, G., Dymov, O., Maddrell-Mander, S., McLean, C., Wenkel, F., Müller, L., Mohamud, J.H., Parviz, A., Craig, M., Koziarski, M., Lu, J., Zhu, Z., Gabellini, C., Klaser, K., Dean, J., Wognum, C., Sypetkowski, M., Rabusseau, G., Rabbany, R., Tang, J., Morris, C., Ravanelli, M., Wolf, G., Tossou, P., Mary, H., Bois, T., Fitzgibbon, A.W., Banaszewski, B., Martin, C., Masters, D.: Towards foundational models for molecular learning on large-scale multi-task datasets. In: The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=Zc2aIcucwc
2024
Closest in time.
2024
Closest in time.
Lupo Pasini, M., Choi, J.Y., Zhang, P., Baker, J., Science, U.O.: HydraGNN v3.0, Version v3.0 (2024). https://doi.org/10.11578/dc.20240131.1 . https://www.osti.gov/biblio/2283293
2024
Closest in time.
Batatia, I., Benner, P., Chiang, Y., Elena, A.M., Kovács, D.P., Riebesell, J., Advincula, X.R., Asta, M., Avaylon, M., Baldwin, W.J., Berger, F., Bernstein, N., Bhowmik, A., Blau, S.M., Cărare, V., Darby, J.P., De, S., Pia, F.D., Deringer, V.L., Elijošius, R., El-Machachi, Z., Falcioni, F., Fako, E., Ferrari, A.C., Genreith-Schriever, A., George, J., Goodall, R.E.A., Grey, C.P., Grigorev, P., Han, S., Handley, W., Heenen, H.H., Hermansson, K., Holm, C., Jaafar, J., Hofmann, S., Jakob, K.S., Jung, H., Kapil, V., Kaplan, A.D., Karimitari, N., Kermode, J.R., Kroupa, N., Kullgren, J., Kuner, M.C., Kuryla, D., Liepuoniute, G., Margraf, J.T., Magdău, I.-B., Michaelides, A., Moore, J.H., Naik, A.A., Niblett, S.P., Norwood, S.W., O’Neill, N., Ortner, C., Persson, K.A., Reuter, K., Rosen, A.S., Schaaf, L.L., Schran, C., Shi, B.X., Sivonxay, E., Stenczel, T.K., Svahn, V., Sutton, C., Swinburne, T.D., Tilly, J., Oord, C., Varga-Umbrich, E., Vegge, T., Vondrák, M., Wang, Y., Witt, W.C., Zills, F., Csányi, G.: A foundation model for atomistic materials chemistry. ArXiv (arXiv:2401.00096) (2024) https://doi.org/arXiv:2401.00096
2024
Closest in time.
Baker, J., Pasini, M.L., Hauck, C.: Invariant features for accurate predictions of quantum chemical uv-vis spectra of organic molecules. In: SoutheastCon 2024, pp. 311–320 (2024). https://doi.org/10.1109/SoutheastCon52093.2024.10500060
2024
Closest in time.
Egele, R., Mohr, F., Viering, T., Balaprakash, P.: The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. Neurocomputing 597
2024
Closest in time.
The Top500 List. https://top500.org/lists/top500/2024/06/ . Accessed: 2024-07-17
2024
Closest in time.