Quantum machine learning in chemical space
Jun 6, 2025
2:30PM to 3:30PM
Date/Time
Date(s) - 06/06/2025
2:30 pm - 3:30 pm
Categories
Prof. Anatole von Lilienfeld
Dept. of Materials Science and Engineering, University of Toronto
Many of the most relevant observables of matter depend explicitly on atomistic and electronic structure, rendering physics-based approaches to chemistry and materials necessary. Unfortunately, due to the combinatorial scaling of the number of chemicals and potential reaction settings, gaining a holistic and rigorous understanding through exhaustive quantum and statistical mechanics-based sampling is prohibitive — even when using high-performance computers. Accounting for explicit and implicit dependencies and correlations, however, will not only deepen our fundamental understanding but also benefit exploration campaigns (computational and experimental). I will discuss recently gained insights from my lab elucidating such relationships thanks to alchemical perturbation density functional theory and supervised machine learning.
Speaker bio:
Anatole von Lilienfeld develops methods for the first principles based sampling
of chemical compound space using quantum mechanics, supercomputers, Big
Data, and machine learning. He is also interested in pseudopotentials, per-
turbation theory, symmetry, van der Waals forces, density functional theory,
molecular dynamics, and nuclear quantum effects. He has authored or co-
authored over 130 peer reviewed publications, and an h-index of 66 (according
to Google Scholar). Anatole was awarded the L¨owdin lecture award at the Uni-
versity of Uppsala, the Feynman Theory Prize by the Foresight Institute, and a
Woodward lecture at Harvard University. Anatole received external support for
his research including grants from NSERC, CFREF, and CFI in Canada, an
ERC consolidator grant in the EU, and an assistant professorship grant by the
Swiss National Science Foundation. Anatole served as the inaugural Editor in
Chief of MLST (IOP Publishing), as an Associate Editor for JCTC (ACS),
and on the Editorial Advisory board of Scientific Data (Nature) and Science
Advances (AAAS). Currently, he serves as an Editor with JACS (ACS), and
as a Senior Scientific Advisor for the machine learning journals published by
IOPP.
Anatole is a Full Professor at University of Toronto, the Ed Clark Chair of
Advanced Materials, and a CIFAR AI chair at the Vector Institute, Canada.
In 2022, he became a Visiting Professor at the Machine Learning group at
TU Berlin, after serving as a Full Professor of Computational Materials Dis-
covery at the Faculty of Physics, University of Vienna, Austria from 2020 to
2022. Prior to that, Anatole was awarded tenure and a promotion to Asso-
ciate Professor of Physical Chemistry at the Department of Chemistry at the
University of Basel in 2019, after he had returned as a Tenure Track Assistant
Professor from the Free University of Brussels (where he served briefly as an
Associate Professor in 2016). He was a Swiss National Science Foundation
Assistant Professor in the Institute of Physical Chemistry at the Department
of Chemistry at the University of Basel from 2013-2015. Prior to that he was
a member of scientific staff at the Argonne National Laboratory’s Leadership
Computing Facility in Illinois which hosts one of the world’s largest supercom-
puters accessible to open science and research. In spring 2011 he chaired the
3 months program, “Navigating Chemical Compound Space for Materials and
Bio Design”, at the Institute for Pure and Applied Mathematics, UCLA, Cali-
fornia. From 2007 to 2010 he was a Distinguished Harry S. Truman Fellow at
Sandia National Laboratories, New Mexico. Anatole carried out postdoctoral
research at the Max-Planck Institute for Polymer Research (2007) and at New
York University (2006). He received a PhD in computational chemistry from
EPF Lausanne in 2005. He performed his diploma thesis work at ETH Zurich
and the University of Cambridge (UK). He studied chemistry at ETH Zurich,
the Ecole de Chimie, Polymers, et Materiaux in Strasbourg, and the University
of Leipzig.
In-Person: ABB 102
Online: https://mcmaster.zoom.us/j/96086389531