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The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation.
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2017
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A. P. Bartók, S. De, C. Poelking, N. Bernstein, J. R. Kermode, G. Csányi, and M. Ceriotti, “Machine learning unifies the modeling of materials and molecules,” Sci. Adv. 3
2017
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F. A. Faber, L. Hutchison, B. Huang, J. Gilmer, S. S. Schoenholz, G. E. Dahl, O. Vinyals, S. Kearnes, P. F. Riley, and O. A. von Lilienfeld, “Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error,” J. Chem. Theory Comput. 13
2017
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A. Grisafi, D. M. Wilkins, G. Csányi, and M. Ceriotti, “Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems,” Phys. Rev. Lett. 120
2018
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M. J. Willatt, F. Musil, and M. 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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2018
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2018
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B. Anderson, T. S. Hy, and R. Kondor, “Cormorant: Covariant Molecular Neural Networks,” in NeurIPS (2019) p. 10
2019
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2020
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2020
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J. Nigam, S. Pozdnyakov, and M. Ceriotti, “Recursive evaluation and iterative contraction of N -body equivariant features,” J. Chem. Phys. 153
2020
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S. Pozdnyakov, “NICE libraries,” https://github.com/cosmo-epfl/nice (2020)
2020
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B. A. Helfrecht, R. K. Cersonsky, G. Fraux, and M. Ceriotti, “Structure-property maps with Kernel principal covariates regression,” Mach. Learn.: Sci. Technol. 1
2020
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L. Himanen, M. O. J. Jäger, E. V. Morooka, F. Federici Canova, Y. S. Ranawat, D. Z. Gao, P. Rinke, and A. S. Foster, “DScribe: Library of descriptors for machine learning in materials science,” Computer Physics Communications 247
2020
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2020
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A. Goscinski, G. Fraux, G. Imbalzano, and M. Ceriotti, “The role of feature space in atomistic learning,” Mach. Learn.: Sci. Technol. 2
2021
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R. K. Cersonsky, B. Helfrecht, E. A. Engel, S. Kliavinek, and M. Ceriotti, “Improving Sample and Feature Selection with Principal Covariates Regression,” Mach. Learn.: Sci. Technol. (2021), 10.1088/2632-2153/abfe7c
2021
Closest in time.