O. Anatole von Lilienfeld

19 papers Journal 18Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions.
CoRR
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price, Victor Posligua, Stephan Roche, O. Anatole von Lilienfeld, Konstantin S. Novoselov, Xavier Bresson, Gianmarco Mengaldo, Xudong Chen, Terence J. O'Kane, Emily R. Lines, Matthew J. Allen, Amandine E. Debus, Clayton Miller, Jiayu Zhou, Hiroko H. Dodge, David Rousseau, Andrey Ustyuzhanin, Ziyun Yan, Mario Lanza, Fabio Sciarrino, Ryo Yoshida, Zhidong Leong, Teck Leong Tan, Qianxiao Li, Adil Kabylda, Igor Poltavsky, Alexandre Tkatchenko, Sherif Abdulkader Tawfik, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Kristin A. Persson, Bryant Y. Li, Vir Karan, Chenru Duan, Haojun Jia, Qiyuan Zhao, Hiroyuki Hayashi, Atsuto Seko, Isao Tanaka, Omar M. Yaghi, Tim Gould, Bun Chan, Stefan Vuckovic, Tianbo Li, Min Lin, Zehcen Tang, Yang Li, Yong Xu, Amrita Joshi, Xiaonan Wang, Leonard W. T. Ng, Sergei V. Kalinin, Mahshid Ahmadi, Jiyizhe Zhang, Shuyuan Zhang, Alexei A. Lapkin, Ming Xiao, Zhe Wu, Kedar Hippalgaonkar, Limsoon Wong, Lorenzo Bastonero, Nicola Marzari, Dorye Luis Esteras Cordoba, Andrei Tomut, Alba Quinones Andrade, Jose-Hugo Garcia
2025 J jnl
Calculated solvation and ionization energies for thousands of organic molecules relevant to battery design.
Mach. Learn. Sci. Technol.
Jan Weinreich, Konstantin Karandashev, Daniel J. Arismendi-Arrieta, Kersti Hermansson, O. Anatole von Lilienfeld
2025 J jnl
Gradient-Guided Furthest Point Sampling for Robust Training Set Selection.
CoRR
Morris Trestman, Stefan Gugler, Felix A. Faber, O. Anatole von Lilienfeld
2025 J jnl
High- T c superconductor candidates proposed by machine learning.
Mach. Learn. Sci. Technol.
Siwoo Lee, Jason Hattrick-Simpers, Young-June Kim, O. Anatole von Lilienfeld
2024 J jnl
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials.
Mach. Learn. Sci. Technol.
Siwoo Lee, Stefan N. Heinen, Danish Khan, O. Anatole von Lilienfeld
2024 J jnl
Data-Error Scaling in Machine Learning on Natural Discrete Combinatorial Mutation-prone Sets: Case Studies on Peptides and Small Molecules.
CoRR
Vanni Doffini, O. Anatole von Lilienfeld, Michael A. Nash
2024 J jnl
Reducing training data needs with minimal multilevel machine learning (M3L).
Mach. Learn. Sci. Technol.
Stefan N. Heinen, Danish Khan, Guido Falk von Rudorff, Konstantin Karandashev, Daniel J. Arismendi-Arrieta, Alastair J. A. Price, Surajit Nandi, Arghya Bhowmik, Kersti Hermansson, O. Anatole von Lilienfeld
2023 J jnl
Encrypted machine learning of molecular quantum properties.
Mach. Learn. Sci. Technol.
Jan Weinreich, Guido Falk von Rudorff, O. Anatole von Lilienfeld
2023 J jnl
Improved decision making with similarity based machine learning: applications in chemistry.
Mach. Learn. Sci. Technol.
Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
2022 J jnl
Encrypted machine learning of molecular quantum properties.
CoRR
Jan Weinreich, Guido Falk von Rudorff, O. Anatole von Lilienfeld
2021 J jnl
An assessment of the structural resolution of various fingerprints commonly used in machine learning.
Mach. Learn. Sci. Technol.
Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen, Felix A. Faber, Emir Kocer, Sandip De, Jörg Behler, O. Anatole von Lilienfeld, Stefan Goedecker
2020 J jnl
Introducing Machine Learning: Science and Technology.
Mach. Learn. Sci. Technol.
O. Anatole von Lilienfeld
2020 J jnl
Machine learning the computational cost of quantum chemistry.
Mach. Learn. Sci. Technol.
Stefan N. Heinen, Max Schwilk, Guido Falk von Rudorff, O. Anatole von Lilienfeld
2020 J jnl
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.
Mach. Learn. Sci. Technol.
Julia Westermayr, Felix A. Faber, Anders S. Christensen, O. Anatole von Lilienfeld, Philipp Marquetand
2020 J jnl
On the role of gradients for machine learning of molecular energies and forces.
Mach. Learn. Sci. Technol.
Anders S. Christensen, O. Anatole von Lilienfeld
2020 J jnl
Thousands of reactants and transition states for competing E2 and S$_\mathrm{N}$2 reactions.
Mach. Learn. Sci. Technol.
Guido Falk von Rudorff, Stefan N. Heinen, Marco Bragato, O. Anatole von Lilienfeld
2020 J jnl
Wasserstein metric for improved quantum machine learning with adjacency matrix representations.
Mach. Learn. Sci. Technol.
Onur Çaylak, O. Anatole von Lilienfeld, Björn Baumeier
2012 — conf
Learning Invariant Representations of Molecules for Atomization Energy Prediction.
NIPS
Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Klaus-Robert Müller
2012 J jnl
Modeling of molecular atomization energies using machine learning.
J. Cheminformatics
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld