O. Anatole von Lilienfeld

University of Toronto — Canada

Materials Science · Physical Sciences

57h-index16.8kcitations264works4.22yr avg
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Research Topics

Machine Learning in Materials Science(162), Computational Drug Discovery Methods(103), Advanced Chemical Physics Studies(87), Various Chemistry Research Topics(35), Protein Structure and Dynamics(24)

Publications264 total

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
arXiv (Cornell University)·2025Open Access
Alchemical diastereomers from antisymmetric alchemical perturbations
The Journal of Chemical Physics·2025
Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
arXiv (Cornell University)·2025Open Access
Hammett-Inspired Product Baseline for Data-Efficient Δ-ML in Chemical Space
Journal of Chemical Theory and Computation·2025
Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br
Scientific Data·2025· 4 citedOpen Access
High- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>T</mml:mi> <mml:mrow> <mml:mi>c</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> superconductor candidates proposed by machine learning
Machine Learning Science and Technology·2025Open Access
Calculated solvation and ionization energies for thousands of organic molecules relevant to battery design
Machine Learning Science and Technology·2025Open Access
Antisymmetry rules of response properties in certain chemical spaces
The Journal of Chemical Physics·2025· 1 cited
Artificial intelligence for advanced functional materials: exploring current and future directions
Journal of Physics Materials·2025· 8 citedOpen Access
Antisymmetry rules of response properties in certain chemical spaces
arXiv (Cornell University)·2025Open Access
Adapting hybrid density functionals with machine learning
Science Advances·2025· 19 citedOpen Access
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for Δ-machine learning
The Journal of Chemical Physics·2025· 4 cited
The alchemical integral transform revisited
The Journal of Chemical Physics·2025· 2 cited
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
Alchemical insights into approximately quadratic energies of iso-electronic atoms
The Journal of Chemical Physics·2024· 4 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for $Δ$-machine learning
arXiv (Cornell University)·2024· 1 citedOpen Access
Alchemical insights into approximately quadratic energies of iso-electronic atoms
arXiv (Cornell University)·2024Open Access
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Frequent Co-authors

Matthias Rupp(8), Alexandre Tkatchenko(8), Raghunathan Ramakrishnan(7), Ursula Röthlisberger(6), Klaus‐Robert Müller(5), Bing Huang(5), Ivano Tavernelli(5), Felix A. Faber(4), Pavlo O. Dral(3), Katja Hansen(3), Daniel Sebastiani(3), Guido Falk von Rudorff(3), Franziska Biegler(2), Grégoire Montavon(2), Alexander Lindmaa(2), Rickard Armiento(2), Stefan Heinen(2), Kieron Burke(2), I‐Chun Lin(2), Maurı́cio D. Coutinho-Neto(2)