Anatole is a Full Professor at the University of Toronto and holds the Clark Chair of Advanced Materials at the Vector Institute. In spring 2022, he’s been a Visiting Professor at the Machine Learning group at TU Berlin after serving as a Full Professor of Computational Materials Discovery at the Faculty of Physics, University of Vienna, Austria, from 2020 onward. Prior to that, Anatole was awarded tenure and a promotion to Associate Professor of Physical Chemistry at the Department of Chemistry at the University of Basel in 2019, after returning from the Free University of Brussels (where he served briefly as an Associate Professor in 2016) to Basel as a Tenure Track Assistant Professor. He held a Swiss National Science Foundation Assistant Professorship 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 supercomputers accessible to open science and research. In the spring of 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, California. 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 Zürich and the University of Cambridge (UK). He pursued his undergraduate studies at ETH Zuerich, École de Chimie, Polymers, et Matèriaux in Strasbourg, and University of Leipzig.
Professor, Department of Chemistry, University of Toronto
Professor, Department of Materials Science & Engineering, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Clark Chair in Advanced Materials
Research Interests
- Chemical Compound Space
- Quantum Machine Learning
- Computational Materials Design & Discovery
- Experimental Design
- Chemical Reactions
Highlights
- Member, Acceleration Consortium
Publications
An assessment of the structural resolution of various fingerprints commonly used in machine learning
2021
Ab initio machine learning in chemical compound space
2021
Machine learning based energy-free structure predictions of molecules, transition states, and solids
2021
Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation
2021
Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
2021
Introduction: Machine Learning at the Atomic Scale
2021
Simplifying inverse materials design problems for fixed lattices with alchemical chirality
2021
Density Functional Geometries and Zero-Point Energies in Ab Initio Thermochemical Treatments of Compounds with First-Row Atoms (H, C, N, O, F)
2021
Conformer-specific polar cycloaddition of dibromobutadiene with trapped propene ions
2021
Elucidating an Atmospheric Brown Carbon Species—Toward Supplanting Chemical Intuition with Exhaustive Enumeration and Machine Learning
2021
An orbital-based representation for accurate quantum machine learning
2022
Relative energies without electronic perturbations via integral transform
2019
Non-covalent interactions between molecular dimers (S66) in electric fields
2022
Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations
2023
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
2023
Reducing training data needs with minimal multilevel machine learning (M3L)
2023
Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design
2023