AI3SD Video: Quantum machine learning

von Lilenfeld, Anatole (2021) AI3SD Video: Quantum machine learning. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI3SD Autumn Seminar Series 2021. 13 Oct - 15 Dec 2021. (doi:10.5258/SOTON/AI3SD0165).

Record type: Conference or Workshop Item (Other)

Abstract

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 quantum mechanical observables throughout compound space. Numerical results indicate promising performance in terms of efficiency, accuracy, scalability and transferability.

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Published date: 10 November 2021

Additional Information: O. Anatole von Lilienfeld is a full university professor of computational materials discovery at the Faculty of Physics at the University of Vienna. Research in his laboratory deals with the development of improved methods for a first-principles-based understanding of chemical compound space using perturbation theory, machine learning, and high-performance computing. Previously, he was an associate and assistant professor at the University of Basel, Switzerland, and at the Free University of Brussels, Belgium. From 2007 to 2013, he worked for Argonne and Sandia National Laboratories after postdoctoral studies with Mark Tuckerman at New York University and at the Institute for Pure and Applied Mathematics at the University of California Los Angeles. In 2005, he was awarded a Ph.D. in computational chemistry from EPF Lausanne under the guidance of Ursula Rothlisberger. His diploma thesis work was done at ETH Zurich with Martin Quack and the University of Cambridge with Nicholas Handy. He studied chemistry at ETH Zurich, the Ecole de Chimie Polymers et Materiaux in Strasbourg, and the University of Leipzig. He serves as editor in chief of the IOP journal Machine Learning: Science and Technology and on the editorial board of Science Advances. He has been on the editorial board of Nature’s Scientific Data from 2014 to 2019. He was the chair of the long IPAM “UCLA program ‘Navigating Chemical Compound Space for Materials and Bio Design’” which took place in 2011. He is the recipient of multiple awards including the Swiss National Science foundation postdoctoral grant (2005), Harry S. Truman postdoctoral fellowship (2007), Thomas Kuhn Paradigm Shift award (2013), Swiss National Science professor fellowship (2013), Odysseus grant from Flemish Science foundation (2016), ERC consolidator grant (2017), and Feynman Prize in Nanotechnology (2018).

Venue - Dates: AI3SD Autumn Seminar Series 2021, 2021-10-13 - 2021-12-15

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