O. Anatole von Lilienfeld
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Anatole von Lilienfeld is a Canada CIFAR AI Chair at the Vector Institute. He leads an interdisciplinary team at University of Toronto, working on theoretical and computational methods for the quantum mechanics based exploration of chemical compound space. While his research falls into the realm of physical chemistry, it relies on theory and computations as well as work based in physics, mathematics, and computer sciences. Rather than conducting primary experiments, von Lilienfeld’s team compares computational predictions to experimental results reported in the scientific literature, or via direct collaborations with experimentalists.
Von Lilienfeld’s work deals with chemical compound space, relying on conceptual as well as molecular grand-canonical ensemble density functional theory, ligand design and intermolecular binding, quantum chemistry (solving approximate Schroedinger equations with density functional theory, semi-empirical methods, or post-Hartree Fock), machine learning (supervised learning), statistical mechanics (using electronic, atomistic, coarse-grained & empirical force-fields and multi-scaling), molecules, liquids, and solids including molecular crystals and defects, and supercomputing.
Awards
- Löwdin Lecturer Award, Uppsala University, 2021
- Feynman Prize Theory, Foresight Institute, 2018
- Woodward Lecturer, Harvard Chemistry, 2014
- Thomas Kuhn Paradigm Shift Award, OpenEye, 2013
- Award for excellence, LDRD Sandia Laboratories, 2010
Relevant Publications
- A Jamzad, A Santilli, S Varma, J Engle, M Kauffmann, J Rudan, G Fichtinger, P Mousavi. (2021). Graph Transformers for characterization and interpretation of surgical margins. Medical Image Computing and Computer Assisted Interventions (MICCAI).
- D Lemm, D., GF von Rudorff, OA von Lilienfeld. (2021). Machine learning based energy-free structure predictions of molecules, transition states, and solids. Nature Communications, 12.
- B Huang, OA von Lilienfeld. (2020). Quantum machine learning using atom-in-molecule-based fragments selected on the fly. Nature Chemistry, 12.
- FA Faber, AS Christensen, B Huang, OA von Lilienfeld. (2018). Alchemical and structural distribution based representation for universal quantum machine learning. Journal of Chemical Physics.
- FA Faber, A Lindmaa, OA von Lilienfeld, R Armiento. (2016). Machine Learning Energies of 2 Million (ABC2D6) Elpasolite Crystals. Physical Review Letters.
- M Rupp, A Tkatchenko, KR Müller, OA von Lilienfeld. (2012). Fast and accurate modeling of molecular atomization energies with machine learning. Physical Review Letters.
Support Us
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.