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Modeling of molecular atomization energies using machine learning., , , and . J. Cheminformatics, 4 (S-1): 33 (2012)On the role of gradients for machine learning of molecular energies and forces., and . Mach. Learn. Sci. Technol., 1 (4): 45018 (2020)An assessment of the structural resolution of various fingerprints commonly used in machine learning., , , , , , , , and . Mach. Learn. Sci. Technol., 2 (1): 15018 (2021)Encrypted machine learning of molecular quantum properties., , and . CoRR, (2022)Machine learning the computational cost of quantum chemistry., , , and . Mach. Learn. Sci. Technol., 1 (2): 25002 (2020)Neural networks and kernel ridge regression for excited states dynamics of CH2NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models., , , , and . Mach. Learn. Sci. Technol., 1 (2): 25009 (2020)Encrypted machine learning of molecular quantum properties., , and . Mach. Learn. Sci. Technol., 4 (2): 25017 (June 2023)Wasserstein metric for improved quantum machine learning with adjacency matrix representations., , and . Mach. Learn. Sci. Technol., 1 (3): 03 (2020)Learning Invariant Representations of Molecules for Atomization Energy Prediction., , , , , , , , and . NIPS, page 449-457. (2012)Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, , , and . Phys. Rev. Lett., 108 (5): 058301 (January 2012)