O Anatole von Lilienfeld

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 37

Top Topics

Density Functional Theory

Machine Learning

Advance Care Planning

Top Venues

The Journal of chemical physics

Journal of chemical theory and computation

Physical chemistry chemical physics : PCCP

Nature communications

This page only lists publications with an associated author ORCID identifier.

Publications

Štěpán Sršeň, O Anatole von Lilienfeld, Petr Slavíček
Fast and accurate excited states predictions: machine learning and diabatization. Physical chemistry chemical physics : PCCP 26 (5) (2024)
Štěpán Sršeň, O Anatole von Lilienfeld, Petr Slavíček
Fast and accurate excited states predictions: machine learning and diabatization. Physical chemistry chemical physics : PCCP (2024)
Štěpán Sršeň, O Anatole von Lilienfeld, Petr Slavíček
Fast and accurate excited states predictions: machine learning and diabatization. Physical chemistry chemical physics : PCCP 26 (5) (2024)
Štěpán Sršeň, O Anatole von Lilienfeld, Petr Slavíček
Fast and accurate excited states predictions: machine learning and diabatization. Physical chemistry chemical physics : PCCP 26 (5) (2024)
Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, Ganna Gryn'ova, Bartosz A Grzybowski, Geun Ho Gu, Jason E Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, Yousung Jung, Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Changyoung Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O Anatole von Lilienfeld, Aron Walsh
Accelerated chemical science with AI. Digital discovery 3 (1) (2023)
Konstantin Karandashev, Jan Weinreich, Stefan Heinen, Daniel Jose Arismendi Arrieta, Guido Falk von Rudorff, Kersti Hermansson, O Anatole von Lilienfeld
Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design. Journal of chemical theory and computation (2023)
Michael J Sahre, Guido Falk von Rudorff, O Anatole von Lilienfeld
Quantum Alchemy Based Bonding Trends and Their Link to Hammett's Equation and Pauling's Electronegativity Model. Journal of the American Chemical Society (2023)
Ardita Kilaj, Silvan Käser, Jia Wang, Patrik Straňák, Max Schwilk, Lei Xu, O Anatole von Lilienfeld, Jochen Küpper, Markus Meuwly, Stefan Willitsch
Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions. Physical chemistry chemical physics : PCCP (2023)
Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, Ganna Gryn'ova, Bartosz A Grzybowski, Geun Ho Gu, Jason E Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, Yousung Jung, Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Changyoung Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O Anatole von Lilienfeld, Aron Walsh
Accelerated chemical science with AI. Digital discovery 3 (1) (2023)
Bing Huang, O Anatole von Lilienfeld, Jaron T Krogel, Anouar Benali
Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML. Journal of chemical theory and computation (2023)
Michael J Sahre, Guido Falk von Rudorff, O Anatole von Lilienfeld
Quantum Alchemy Based Bonding Trends and Their Link to Hammett's Equation and Pauling's Electronegativity Model. Journal of the American Chemical Society 145 (10) (2023)
Danish Khan, Stefan Heinen, O Anatole von Lilienfeld
Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations. The Journal of chemical physics 159 (3) (2023)
Bing Huang, Guido Falk von Rudorff, O Anatole von Lilienfeld
The central role of density functional theory in the AI age. Science (New York, N.Y.) 381 (6654) (2023)
Nicholas J Browning, Felix A Faber, O Anatole von Lilienfeld
GPU-accelerated approximate kernel method for quantum machine learning. The Journal of chemical physics 157 (21) (2022)
Bernard Mazouin, Alexandre Alain Schöpfer, O Anatole von Lilienfeld
Selected machine learning of HOMO-LUMO gaps with improved data-efficiency. Materials advances 3 (22) (2022)
Bernard Mazouin, Alexandre Alain Schöpfer, O Anatole von Lilienfeld
Selected machine learning of HOMO-LUMO gaps with improved data-efficiency. Materials advances 3 (22) (2022)
Stefan Heinen, Guido Falk von Rudorff, O Anatole von Lilienfeld
Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning. The Journal of chemical physics 157 (22) (2022)
Giorgio Domenichini, O Anatole von Lilienfeld
Alchemical geometry relaxation. The Journal of chemical physics 156 (18) (2022)
Jan Weinreich, Dominik Lemm, Guido Falk von Rudorff, O Anatole von Lilienfeld
Ab initio machine learning of phase space averages. The Journal of chemical physics 157 (2) (2022)
Guido Falk von Rudorff, O Anatole von Lilienfeld
Simplifying inverse materials design problems for fixed lattices with alchemical chirality. Science advances 7 (21) (2021)
Bing Huang, O Anatole von Lilienfeld
Ab Initio Machine Learning in Chemical Compound Space. Chemical reviews 121 (16) (2021)
Dominik Lemm, Guido Falk von Rudorff, O Anatole von Lilienfeld
Machine learning based energy-free structure predictions of molecules, transition states, and solids. Nature communications 12 (1) (2021)
Enrico Tapavicza, Guido Falk von Rudorff, David O De Haan, Mario Contin, Christian George, Matthieu Riva, O Anatole von Lilienfeld
Elucidating an Atmospheric Brown Carbon Species-Toward Supplanting Chemical Intuition with Exhaustive Enumeration and Machine Learning. Environmental science & technology 55 (12) (2021)
Silvan Käser, Debasish Koner, Anders S Christensen, O Anatole von Lilienfeld, Markus Meuwly
Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet. The journal of physical chemistry. A 124 (42) (2020)
Marco Bragato, Guido Falk von Rudorff, O Anatole von Lilienfeld
Data enhanced Hammett-equation: reaction barriers in chemical space. Chemical science 11 (43) (2020)
O Anatole von Lilienfeld, Kieron Burke
Retrospective on a decade of machine learning for chemical discovery. Nature communications 11 (1) (2020)
Giorgio Domenichini, Guido Falk von Rudorff, O Anatole von Lilienfeld
Effects of perturbation order and basis set on alchemical predictions. The Journal of chemical physics 153 (14) (2020)
Bing Huang, O Anatole von Lilienfeld
Quantum machine learning using atom-in-molecule-based fragments selected on the fly. Nature chemistry 12 (10) (2020)
Guido Falk von Rudorff, O Anatole von Lilienfeld
Atoms in Molecules from Alchemical Perturbation Density Functional Theory. The journal of physical chemistry. B 123 (47) (2019)
Anders S Christensen, Felix A Faber, O Anatole von Lilienfeld
Operators in quantum machine learning: Response properties in chemical space. The Journal of chemical physics 150 (6) (2019)
Peter Zaspel, Bing Huang, Helmut Harbrecht, O Anatole von Lilienfeld
Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited. Journal of chemical theory and computation 15 (3) (2019)
Julian J Kranz, Maximilian Kubillus, Raghunathan Ramakrishnan, O Anatole von Lilienfeld, Marcus Elstner
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning. Journal of chemical theory and computation 14 (5) (2018)
O Anatole von Lilienfeld
Quantum Machine Learning in Chemical Compound Space. Angewandte Chemie (International ed. in English) 57 (16) (2018)
Stijn Fias, K Y Samuel Chang, O Anatole von Lilienfeld
Alchemical Normal Modes Unify Chemical Space. The journal of physical chemistry letters 10 (1) (2018)
Diana N Tahchieva, Dirk Bakowies, Raghunathan Ramakrishnan, O Anatole von Lilienfeld
Torsional Potentials of Glyoxal, Oxalyl Halides, and Their Thiocarbonyl Derivatives: Challenges for Popular Density Functional Approximations. Journal of chemical theory and computation 14 (9) (2018)
Karthikeyan Saravanan, John R Kitchin, O Anatole von Lilienfeld, John A Keith
Alchemical Predictions for Computational Catalysis: Potential and Limitations. The journal of physical chemistry letters 8 (20) (2017)
Felix A Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S Schoenholz, George E Dahl, Oriol Vinyals, Steven Kearnes, Patrick F Riley, O Anatole von Lilienfeld
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. Journal of chemical theory and computation 13 (11) (2017)