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The Promise and Rise of Machine Learning in Chemistry and Physics LECTURE

Date: Wed 5 Feb 2020, 12:00 - 13:00

Location: Arts One Lecture Theatre, Mile End Campus, Queen Mary University of London

Abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding (quantum) molecules and materials? Aiming towards a unified machine learning (ML) model of quantum interactions, I will discuss the potential and challenges for using ML techniques in chemistry and physics. ML methods can not only accurately estimate molecular properties of large datasets, but they can also lead to new insights into chemical similarity, aromaticity, reactivity, and molecular dynamics. While the potential of machine learning for revealing insights into molecules and materials is high, I will conclude my talk by discussing the many remaining challenges.

The speaker: Alexandre Tkatchenko is Professor of Theoretical Chemical Physics at the University of Luxembourg and Visiting Professor at the Berlin Big Data Centre. Professor Tkatchenko obtained his bachelor degree in Computer Science and a PhD in Physical Chemistry at the Universidad Autonoma Metropolitana in Mexico City. He was an Alexander von Humboldt Fellow at the Fritz Haber Institute of the Max Planck Society in Berlin from 2008 and 2010 and led an independent research group at the same institute between 2011 to 2016. He has given more than 230 invited talks, seminars and colloquia worldwide, published more than 150 articles in peer-reviewed academic journals (h-index=57), and serves on editorial boards of two society journals: Physical Review Letters (APS) and Science Advances (AAAS). He received a number of awards, including elected Fellow of the American Physical Society, the Gerhard Ertl Young Investigator Award of the German Physical Society, and two flagship grants from the European Research Council: a Starting Grant in 2011 and a Consolidator Grant in 2017.

Professor Tkatchenko group pushes the boundaries of quantum mechanics, statistical mechanics, and machine learning to develop efficient methods to enable accurate modelling and obtain new insights into complex materials.

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