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Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space.
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
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Cormorant: Covariant molecular neural networks
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
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Generative models for graph-based protein design
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Set transformer: A framework for attention-based permutation-invariant neural networks
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Fake news detection on social media using geometric deep learning
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Weisfeiler and leman go neural: Higher-order graph neural networks
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
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PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges
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Computational reconstruction of atomistic protein structures from coarse-grained models
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Unveiling the predictive power of static structure in glassy systems
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Semi-supervised learning and graph neural networks for fake news detection
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Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
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On the universality of rotation equivariant point cloud networks
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User’s guide for test (version 5.1)(toxicity estimation software tool): a program to estimate toxicity from molecular structure
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SE(3)-transformers: 3D roto-translation equivariant attention networks
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Directional message passing for molecular graphs
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Learning from protein structure with geometric vector perceptrons
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Transformers are graph neural networks
C. Joshi · 2020
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Self-referencing embedded strings (selfies): A 100% robust molecular string representation
M. Krenn, F. Häse, A. Nigam, P. Friederich, and A. Aspuru-Guzik · 2020
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Incompleteness of atomic structure representations
S. N. Pozdnyakov, M. J. Willatt, A. P. Bartók, C. Ortner, G. Csányi, and M. Ceriotti · 2020
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Learning a continuous representation of 3d molecular structures with deep generative models
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Learning to simulate complex physics with graph networks
A. Sanchez-Gonzalez, J. Godwin, T. Pfaff, R. Ying, J. Leskovec, and P. Battaglia · 2020
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Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network
A. Sherstinsky · 2020
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An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Z. Ren, S. I. P. Tian, J. Noh, F. Oviedo, G. Xing, J. Li, Q. Liang, R. Zhu, A. G. Aberle, S. Sun, et al · 2022
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MGnify: the microbiome sequence data analysis resource in 2023
L. Richardson, B. Allen, G. Baldi, M. Beracochea, M. L. Bileschi, T. Burdett, J. Burgin, J. Caballero-Pérez, G. Cochrane, L. J. Colwell, T. Curtis, A. Escobar-Zepeda, T. A. Gurbich, V. Kale, A. Korobeynikov, S. Raj, A. B. Rogers, E. Sakharova, S. Sanchez, D. J. Wilkinson, and R. D. Finn · 2022
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Learned force fields are ready for ground state catalyst discovery
M. Schaarschmidt, M. Rivière, A. Ganose, J. Spencer, A. Gaunt, J. Kirkpatrick, S. Axelrod, P. Battaglia, and J. Godwin · 2022
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Structure-based drug design with equivariant diffusion models
A. Schneuing, Y. Du, C. Harris, A. Jamasb, I. Igashov, W. Du, T. Blundell, P. Lió, C. Gomes, M. Welling, et al · 2022
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Supramolecular chemistry
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