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In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry.
A. L. Patterson, Ambiguities in the X-Ray Analysis of Crystal Structures, Phys. Rev. 65
1944
Earlier work this paper cites.
I. Boustani, Systematic ab initio investigation of bare boron clusters:mDetermination of the geometryand electronic structures of B n (n=2–14), Phys. Rev. B 55
1997
Earlier work this paper cites.
M. Boutin and G. Kemper, On reconstructing n-point configurations from the distribution of distances or areas, Advances in Applied Mathematics 32
2004
Earlier work this paper cites.
J. P. Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity , Oxford Master Series in Statistical, Computational, and Theoretical Physics No. 14 (Oxford University Press, Oxford ; New York, 2006)
2006
Earlier work this paper cites.
J. Behler and M. Parrinello, Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces, Phys. Rev. Lett. 98
2007
Earlier work this paper cites.
A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons, Phys. Rev. Lett. 104
2010
Earlier work this paper cites.
J. Behler, Atom-centered symmetry functions for constructing high-dimensional neural network potentials, The Journal of Chemical Physics 134
2011
Earlier work this paper cites.
S. De, A. Willand, M. Amsler, P. Pochet, L. Genovese, and S. Goedecker, Energy Landscape of Fullerene Materials: A Comparison of Boron to Boron Nitride and Carbon, Phys. Rev. Lett. 106
2011
Earlier work this paper cites.
F. Pietrucci and W. Andreoni, Graph Theory Meets Ab Initio Molecular Dynamics: Atomic Structures and Transformations at the Nanoscale, Phys. Rev. Lett. 107
2011
Earlier work this paper cites.
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld, Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Phys. Rev. Lett. 108
2012
Earlier work this paper cites.
R. Kakarala, The Bispectrum as a Source of Phase-Sensitive Invariants for Fourier Descriptors: A Group-Theoretic Approach, J Math Imaging Vis 44
2012
Earlier work this paper cites.
J. E. Moussa, Comment on “Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning”, Phys. Rev. Lett. 109
2012
Earlier work this paper cites.
A. Sadeghi, S. A. Ghasemi, B. Schaefer, S. Mohr, M. A. Lill, and S. Goedecker, Metrics for measuring distances in configuration spaces, J. Chem. Phys. 139
2013
Earlier work this paper cites.
A. P. Bartók, R. Kondor, and G. Csányi, On representing chemical environments, Phys. Rev. B 87
2013
Earlier work this paper cites.
K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O. A. von Lilienfeld, A. Tkatchenko, and K.-R. Müller, Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies, J. Chem. Theory Comput. 9
2013
Earlier work this paper cites.
L. Liberti, C. Lavor, N. Maculan, and A. Mucherino, Euclidean Distance Geometry and Applications, SIAM Rev. 56
2014
Earlier work this paper cites.
I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, Euclidean Distance Matrices: Essential theory, algorithms, and applications, IEEE Signal Process. Mag. 32
2015
Earlier work this paper cites.
T. Bereau, D. Andrienko, and O. A. Von Lilienfeld, Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules, J. Chem. Theory Comput. 11
2015
Earlier work this paper cites.
L. Zhu, M. Amsler, T. Fuhrer, B. Schaefer, S. Faraji, S. Rostami, S. A. Ghasemi, A. Sadeghi, M. Grauzinyte, C. Wolverton, and S. Goedecker, A fingerprint based metric for measuring similarities of crystalline structures, J. Chem. Phys. 144
2016
Earlier work this paper cites.
A. V. Shapeev, Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials, Multiscale Model. Simul. 14
2016
Cited alongside, same era.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, Deep sets, in Advances in Neural Information Processing Systems , Vol. 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Curran Associates, Inc., 2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
A. Glielmo, C. Zeni, and A. De Vita, Efficient nonparametric n -body force fields from machine learning, Phys. Rev. B 97
2018
Cited alongside, same era.
B. Parsaeifard, D. Sankar De, A. S. Christensen, F. A. Faber, E. Kocer, S. De, J. Behler, O. Anatole von Lilienfeld, and S. Goedecker, An assessment of the structural resolution of various fingerprints commonly used in machine learning, Mach. Learn.: Sci. Technol. 2
2021
Later among the works it cites.
S. N. Pozdnyakov, L. Zhang, C. Ortner, G. Csányi, and M. Ceriotti, Local invertibility and sensitivity of atomic structure-feature mappings, Open Res Europe 1
2021
Later among the works it cites.
A. Goscinski, G. Fraux, G. Imbalzano, and M. Ceriotti, The role of feature space in atomistic learning, Mach. Learn.: Sci. Technol. 2
2021
Later among the works it cites.
2021
Later among the works it cites.
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L. Zhang, J. Han, H. Wang, R. Car, and W. E, Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics, Phys. Rev. Lett. 120
2018
Cited alongside, same era.
H. Wang, L. Zhang, J. Han, and E. Weinan, Deepmd-kit: A deep learning package for many-body potential energy representation and molecular dynamics, Computer Physics Communications 228
2018
Cited alongside, same era.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Machine learning and the physical sciences, Rev. Mod. Phys. 91
2019
Cited alongside, same era.
A. Tasissa and R. Lai, Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-Rank Matrix Completion, IEEE Trans. Inform. Theory 65
2019
Cited alongside, same era.
M. J. Willatt, F. Musil, and M. Ceriotti, Atom-density representations for machine learning, J. Chem. Phys. 150
2019
Cited alongside, same era.
B. Anderson, T. S. Hy, and R. Kondor, Cormorant: Covariant Molecular Neural Networks, in NeurIPS (2019) p. 10
2019
Cited alongside, same era.
R. Drautz, Atomic cluster expansion for accurate and transferable interatomic potentials, Phys. Rev. B 99
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32 (Curran Associates, Inc., 2019) pp. 8024–8035
2019
Cited alongside, same era.
K. Schütt, O. Unke, and M. Gastegger, Equivariant message passing for the prediction of tensorial properties and molecular spectra, in Int. Conf. Mach. Learn. (PMLR, 2021) pp. 9377–9388
2021
Later among the works it cites.
S. N. Pozdnyakov and M. Ceriotti, Incompleteness of graph neural networks for points clouds in three dimensions, Mach. Learn.: Sci. Technol. 3
2022
Later among the works it cites.
G. Dusson, M. Bachmayr, G. Csányi, R. Drautz, S. Etter, C. van der Oord, and C. Ortner, Atomic cluster expansion: Completeness, efficiency and stability, Journal of Computational Physics 454
2022
Later among the works it cites.
2022
Later among the works it cites.
F. Bigi, K. K. Huguenin-Dumittan, M. Ceriotti, and D. E. Manolopoulos, A smooth basis for atomistic machine learning, J. Chem. Phys. 157
2022
Later among the works it cites.
S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials, Nat Commun 13
2022
Later among the works it cites.
B. Parsaeifard and S. Goedecker, Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions, J. Chem. Phys. 156
2022
Later among the works it cites.
S. N. Pozdnyakov, M. J. Willatt, A. P. Bartók, C. Ortner, G. Csányi, and M. Ceriotti, Comment on “Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions” [J. Chem. Phys. 156, 034302 (2022)], J. Chem. Phys. 157
2022
Later among the works it cites.
D. Widdowson and V. Kurlin, Resolving the data ambiguity for periodic crystals, Advances in Neural Information Processing Systems (Proceedings of NeurIPS 2022) 35
2022
Later among the works it cites.
I. Batatia, D. P. Kovacs, G. N. C. Simm, C. Ortner, and G. Csanyi, MACE: Higher order equivariant message passing neural networks for fast and accurate force fields, in Adv. Neural Inf. Process. Syst. , edited by A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho (2022)
2022
Later among the works it cites.
D. Widdowson and V. Kurlin, Recognizing rigid patterns of unlabeled point clouds by complete and continuous isometry invariants with no false negatives and no false positives, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023) pp. 1275–1284
2023
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2023
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2023
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2023
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