Learning Invariant Representations of Molecules for Atomization Energy Prediction. - Grégoire Montavon; Katja Hansen; Siamac Fazli; Matthias Rupp; Franziska Biegler; Andreas Ziehe; Alexandre Tkatchenko; O. Anatole von Lilienfeld; Klaus-Robert Müller (NIPS, 2012)

Modeling of molecular atomization energies using machine learning. - Matthias Rupp; Alexandre Tkatchenko; Klaus-Robert Müller; O. Anatole von Lilienfeld (J. Cheminformatics, 2012)

Wasserstein metric for improved quantum machine learning with adjacency matrix representations. - Onur Çaylak; O. Anatole von Lilienfeld; Björn Baumeier (Mach. Learn. Sci. Technol., 2020)

On the role of gradients for machine learning of molecular energies and forces. - Anders S. Christensen; O. Anatole von Lilienfeld (Mach. Learn. Sci. Technol., 2020)

Machine learning the computational cost of quantum chemistry. - Stefan N. Heinen; Max Schwilk; Guido Falk von Rudorff; O. Anatole von Lilienfeld (Mach. Learn. Sci. Technol., 2020)

Introducing Machine Learning: Science and Technology. - O. Anatole von Lilienfeld (Mach. Learn. Sci. Technol., 2020)

Thousands of reactants and transition states for competing E2 and S$_ mathrm{N}$2 reactions. - Guido Falk von Rudorff; Stefan N. Heinen; Marco Bragato; O. Anatole von Lilienfeld (Mach. Learn. Sci. Technol., 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. - Julia Westermayr; Felix A. Faber; Anders S. Christensen; O. Anatole von Lilienfeld; Philipp Marquetand (Mach. Learn. Sci. Technol., 2020)

An assessment of the structural resolution of various fingerprints commonly used in machine learning. - Behnam Parsaeifard; Deb Sankar De; Anders S. Christensen; Felix A. Faber; Emir Kocer; Sandip De; Jörg Behler; O. Anatole von Lilienfeld; Stefan Goedecker (Mach. Learn. Sci. Technol., 2021)

Encrypted machine learning of molecular quantum properties. - Jan Weinreich; Guido Falk von Rudorff; O. Anatole von Lilienfeld (Mach. Learn. Sci. Technol., 2023)

Improved decision making with similarity based machine learning: applications in chemistry. - Dominik Lemm; Guido Falk von Rudorff; O. Anatole von Lilienfeld (Mach. Learn. Sci. Technol., 2023)

Data-Error Scaling in Machine Learning on Natural Discrete Combinatorial Mutation-prone Sets: Case Studies on Peptides and Small Molecules. - Vanni Doffini; O. Anatole von Lilienfeld; Michael A. Nash (CoRR, 2024)