Many of the most relevant observables of matter depend explicitly on atomistic and electronic details, rendering a first principles approach to computational materials design mandatory. Alas, even when using high performance computers, brute force high-throughput screening of material candidates is beyond any capacity for all but the simplest systems and properties due to the combinatorial nature of compound space, i.e. all the possible combinations of compositional and structural degrees of freedom. Consequently, efficient exploration algorithms exploit implicit redundancies and correlations. I will discuss recently developed statistical learning based approaches for interpolating relevant chemical properties throughout compound space. Numerical results indicate promising performance in terms of efficiency, accuracy, scalability and transferability.

Bio: Anatole is a CIFAR AI chair and the inaugural Clark Chair in Advanced Materials at the Vector Institute and at University of Toronto. He is also affiliated to the Machine Learning group at Technical University of Berlin where he leads an ERC project on quantum machine learning.

From 2020-2022, he was a Full Professor for Computational Materials Discovery at the Faculty of Physics at the University of Vienna. Prior to that, Anatole held Associate and Assistant Professorship positions at the University of Basel, and the Free University of Brussels. Until 2013, he worked as an Assistant Computational Scientist at the Argonne National Laboratory's Leadership Computing Facility. 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 at UCLA. From 2007 to 2010 Anatole was a Distinguished Harry S. Truman Fellow at Sandia National Laboratories. 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 within an Erasmus exchange program at ETH Zürich and the University of Cambridge. He studied chemistry as an undergraduate at ETH Zürich, the École de Chimie, Polymères, et Matériaux in Strasbourg, and at the University of Leipzig.

Anatole is an Associate Editor of the Journal of Computational and Theoretical Chemistry, and an Editorial Board Member of Kim Reviews. He has served as founding editor for Machine Learning: Science and Technology, as Associate Editor for Science Advances, and as an Editorial Board member for Scientific Data. He serves on the advisory board of SIMPLAIX (KIT, University of Heidelberg, HIT) and of the DOE Frontier Research Center CD4DC (led by University of Chicago).

Anatole is the recipient of the Löwdin Lecturer Award 2021, the Feynman Theory Prize 2018, and the Thomas-Kuhn-Paradigm Shift Award 2013. Among others, Anatole received an ERC Consolidator grant on Quantum Machine Learning (2017) and a Swiss National Science Professorship Award (2013).