Anatole von Lilienfeld

Also known as O. Anatole von Lilienfeld

Anatole von Lilienfeld is a computational chemist who builds machine learning models that predict the properties of molecules and materials from quantum mechanics. Born in the United States and raised in Germany, he earned a PhD in computational chemistry at EPF Lausanne in 2005 and worked at Sandia National Laboratories and Argonne National Laboratory before starting as a professor at the University of Basel in 2013. In 2012 he coauthored a Physical Review Letters paper on machine learning for molecular atomization energies, received a European Research Council Consolidator Grant in 2017, and won the Foresight Institute's Feynman Prize in Theory in 2018. After a professorship at the University of Vienna from 2020, he was named the inaugural Clark Chair in Advanced Materials at Toronto and the Vector Institute in 2021, and in 2023 he coauthored a Science review on density functional theory in the AI age. He is a professor at the University of Toronto, a Canada CIFAR AI Chair at the Vector Institute, and co-lead of the AI and Automation lab at the Acceleration Consortium.

Now at: Vector Institute and Acceleration Consortium
Born: 1976
Badges: Wayfarer
Links: Google Scholar

Years link to their sources.

Timeline

1999–2010

From ETH Zürich to Sandia

Born in the United States, he grows up in Germany and studies chemistry at ETH Zürich. A PhD in computational chemistry at EPF Lausanne follows in 2005, then postdoctoral research at New York University in 2006 and at the Max Planck Institute for Polymer Research in 2007. From 2007 to 2010, he is a Distinguished Harry S. Truman Fellow at Sandia National Laboratories.
2011–2012

Atomization energies by machine learning

He joins the Argonne Leadership Computing Facility in Illinois as an assistant computational scientist in February 2011 and chairs a three-month program, "Navigating Chemical Compound Space for Materials and Bio Design", at UCLA's Institute for Pure and Applied Mathematics in spring 2011. In 2012, a Physical Review Letters paper he coauthors with Matthias Rupp, Alexandre Tkatchenko and Klaus-Robert Müller introduces a machine learning model that predicts atomization energies of organic molecules from nuclear charges and atomic positions only. Cross-validation over more than seven thousand molecules gives a mean absolute error of about 10 kcal/mol.
2013–2019

Assistant professor at Basel

He becomes a Swiss National Science Foundation assistant professor in the Institute of Physical Chemistry at the University of Basel in 2013. After a brief associate professorship at the Free University of Brussels in 2016, he returns to Basel as a tenure-track assistant professor. Tenure and a promotion to associate professor of physical chemistry follow in 2019.
2017–2018

An ERC grant and Feynman Prize

The European Research Council awards him a Consolidator Grant in 2017, while he is at the University of Basel. The Foresight Institute gives him its Feynman Prize in Theory in 2018 for introducing new ways to accelerate quantum mechanics quality predictions across materials compound space by multiple orders of magnitude.
2020–2022

Professor in Vienna

From October 2020 to March 2022, he is a full professor of computational materials discovery at the Faculty of Physics of the University of Vienna. He is Löwdin Lecturer at Uppsala University in 2021, and in April 2022 he becomes a visiting professor and principal investigator of the ERC project QML in the Machine Learning group at TU Berlin.
2021–2022

Inaugural Clark Chair at Toronto

The Acceleration Consortium at the University of Toronto welcomes him in December 2021 as the inaugural Clark Chair in Advanced Materials at the Vector Institute and the university; the consortium combines AI and robotics to accelerate the design and discovery of materials that do not exist yet. He starts as full professor and Ed Clark Chair of Advanced Materials in Toronto in July 2022 and is a Canada CIFAR AI Chair from October 2022.
2023

A Science review of DFT

He publishes "The central role of density functional theory in the AI age" in Science in 2023, with Bing Huang and Guido Falk von Rudorff. The review covers machine learning models trained on synthetic data from density functional theory simulations and describes a future in which robot-led experiments become as fundamental to science as machine learning, computer simulations, traditional theories and human-led experiments.
As of October 2026

Professor and Clark Chair

He is a professor at the University of Toronto, the Clark Chair in Advanced Materials at the Vector Institute and a Canada CIFAR AI Chair. As of May 2023, he co-leads the AI and Automation lab at the Acceleration Consortium. A seminar on quantum machine learning in chemical space at the Brockhouse Institute for Materials Research in June 2025 introduces him as a full professor at the University of Toronto.

Career

Since 2023Acceleration ConsortiumCo-lead
Since 2022University of TorontoFull Professor and Ed Clark Chair in Advanced Materials
Since 2021Vector InstituteInaugural Clark Chair in Advanced Materials, Canada CIFAR AI Chair
From 2022Technische Universität BerlinVisiting Professor and Principal Investigator
2021Uppsala UniversityLöwdin Lecturer
2020–2022University of ViennaFull Professor of Computational Materials Discovery
2016Free University of BrusselsAssociate Professor
2013–2019University of BaselAssistant Professor, Tenure-Track Assistant Professor
2011–2012University of ChicagoAssistant Computational Scientist
2011University of California, Los AngelesProgram Chair
2007Max Planck SocietyPostdoctoral Researcher
2007–2010National Nuclear Security AdministrationDistinguished Harry S. Truman Fellow
2006New York UniversityPostdoctoral Researcher