O. Anatole von Lilienfeld

According to our database, O. Anatole von Lilienfeld authored at least 11 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., December, 2023

Encrypted machine learning of molecular quantum properties.

Jan Weinreich, Guido Falk von Rudorff, O. Anatole von Lilienfeld

Mach. Learn. Sci. Technol., June, 2023

2021

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

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

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

Introducing Machine Learning: Science and Technology.

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

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

Wasserstein metric for improved quantum machine learning with adjacency matrix representations.

Onur Çaylak, O. Anatole von Lilienfeld, Björn Baumeier

Mach. Learn. Sci. Technol., 2020

2012

Modeling of molecular atomization energies using machine learning.

Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld

J. Cheminformatics, 2012

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

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012