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Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies.
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2017
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2018
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2020
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2020
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2020
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2020
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J. Hermann, Z. Schätzle, and F. Noé, “Deep-neural-network solution of the electronic Schrödinger equation,” Nat. Chem. 12
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2021
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B. Jing, S. Eismann, P. Suriana, R. J. L. Townshend, and R. Dror, “Learning from protein structure with geometric vector perceptrons,” in International Conference on Learning Representations (2021)
2021
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H. E. Sauceda, V. Vassilev-Galindo, S. Chmiela, K.-R. Müller, and A. Tkatchenko, “Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature,” Nat. Commun. 12
2021
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