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Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials.
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Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, and Klaus-Robert Müller, “Machine learning of accurate energy-conserving molecular force fields,” Sci. Adv. 3
2017
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Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Csányi, and Michele Ceriotti, “Machine learning unifies the modeling of materials and molecules,” Sci. Adv. 3
2017
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2018
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Felix A. Faber, Anders S. Christensen, Bing Huang, and O. Anatole Von Lilienfeld, “Alchemical and structural distribution based representation for universal quantum machine learning,” J. Chem. Phys. 148
2018
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V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” J. Chem. Theory Comput. 16
2020
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2020
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Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun, “Deep Learning for 3D Point Clouds: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell. 43
2021
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Ying Li, Lingfei Ma, Zilong Zhong, Fei Liu, Michael A. Chapman, Dongpu Cao, and Jonathan Li, “Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review,” IEEE Trans. Neural Netw. Learning Syst. 32
2021
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Andrea Grisafi, David M. Wilkins, Gábor Csányi, and Michele Ceriotti, “Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems,” Phys. Rev. Lett. 120
2018
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Aldo Glielmo, Claudio Zeni, and Alessandro De Vita, “Efficient nonparametric n -body force fields from machine learning,” Phys. Rev. B 97
2018
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Claudio Zeni, Kevin Rossi, Aldo Glielmo, Ádám Fekete, Nicola Gaston, Francesca Baletto, and Alessandro De Vita, “Building machine learning force fields for nanoclusters,” The Journal of Chemical Physics 148
2018
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Michael J. Willatt, Félix Musil, and Michele Ceriotti, “Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements,” Phys. Chem. Chem. Phys. 20
2018
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K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, “SchNet – A deep learning architecture for molecules and materials,” J. Chem. Phys. 148
2018
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Benjamin Coors, Alexandru Paul Condurache, and Andreas Geiger, “Spherenet: Learning spherical representations for detection and classification in omnidirectional images,” in Proceedings of the European conference on computer vision (ECCV) (2018) pp. 518–533
2018
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Wenxuan Wu, Zhongang Qi, and Li Fuxin, “PointConv: Deep Convolutional Networks on 3D Point Clouds,” in 2019 IEEECVF Conf. Comput. Vis. Pattern Recognit. CVPR (IEEE, Long Beach, CA, USA, 2019) pp. 9613–9622
2019
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Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová, “Machine learning and the physical sciences,” Rev. Mod. Phys. 91
2019
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2021
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Michele Ceriotti, Cecilia Clementi, and O. Anatole von Lilienfeld, “Introduction: Machine Learning at the Atomic Scale,” Chem. Rev. 121
2021
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Volker L. Deringer, Albert P. Bartók, Noam Bernstein, David M. Wilkins, Michele Ceriotti, and Gábor Csányi, “Gaussian Process Regression for Materials and Molecules,” Chem. Rev. 121
2021
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Kristof Schütt, Oliver Unke, and Michael Gastegger, “Equivariant message passing for the prediction of tensorial properties and molecular spectra,” in Int. Conf. Mach. Learn. (PMLR, 2021) pp. 9377–9388
2021
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2021
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Sergey N. Pozdnyakov, Liwei Zhang, Christoph Ortner, Gábor Csányi, and Michele Ceriotti, “Local invertibility and sensitivity of atomic structure-feature mappings,” Open Res Europe 1
2021
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Alexander Goscinski, Félix Musil, Sergey Pozdnyakov, Jigyasa Nigam, and Michele Ceriotti, “Optimal radial basis for density-based atomic representations,” J. Chem. Phys. 155
2021
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Michele Ceriotti, “Beyond potentials: Integrated machine learning models for materials,” MRS Bulletin 47
2022
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Geneviève Dusson, Markus Bachmayr, Gábor Csányi, Ralf Drautz, Simon Etter, Cas van der Oord, and Christoph Ortner, “Atomic cluster expansion: Completeness, efficiency and stability,” Journal of Computational Physics 454
2022
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James P. Darby, Dávid P. Kovács, Ilyes Batatia, Miguel A. Caro, Gus L. W. Hart, Christoph Ortner, and Gábor Csányi, “Tensor-reduced atomic density representations,” (2022)
2022
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Ilyes Batatia, David Peter Kovacs, Gregor N. C. Simm, Christoph Ortner, and Gabor Csanyi, “MACE: Higher order equivariant message passing neural networks for fast and accurate force fields,” in Adv. Neural Inf. Process. Syst. , edited by Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (2022)
2022
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Filippo Bigi, Kevin K. Huguenin-Dumittan, Michele Ceriotti, and David E. Manolopoulos, “A smooth basis for atomistic machine learning,” J. Chem. Phys. 157
2022
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Yaolong Zhang, Junfan Xia, and Bin Jiang, “REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems,” J. Chem. Phys. 156
2022
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Philipp Thölke and Gianni De Fabritiis, “Equivariant transformers for neural network based molecular potentials,” in International Conference on Learning Representations (2022)
2022
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Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J Owen, Mordechai Kornbluth, and Boris Kozinsky, “Learning local equivariant representations for large-scale atomistic dynamics,” Nature Communications 14
2023
Closest in time.
Elisabetta Cornacchia, Francesca Mignacco, Rodrigo Veiga, Cédric Gerbelot, Bruno Loureiro, and Lenka Zdeborová, “Learning curves for the multi-class teacher–student perceptron,” Mach. Learn.: Sci. Technol. 4
2023
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