Physics Colloquium, Miguel Marques, University of Halle, Germany

Abstract:

The development of density-functional theory in the 1960s and the
dissemination of computers led to a revolution in materials science. A
third kind of physics, computational physics, emerged to complement
its theoretical and experimental sisters. By solving complex
theoretical models in a computer we had access to quantitative results
for specific systems. These numerical experiments could explain
experiments or be used to predict new materials and their properties.
Nowadays, with the availability of ever faster supercomputers and
novel computer methodologies, we are living what I would call the
second computer revolution in materials science. High throughput
techniques, together with ever faster supercomputers, allow for the
automatic screening of thousands or even millions of hypothetical
materials to find solutions to present technological
challenges. Moreover, machine learning methods are used to accelerate
materials discovery by replacing density-functional theory by
extremely efficient statistical models.

In this talk I summarize our recent attempts to discover and
characterize new materials using these novel approaches. I will start
by motivating why the search for new materials is one of the most
pressing problems nowadays. This is true not only to enable new
technological advances, but also to circumvent availability,
environmental, economical, and political problems related to many
chemical elements.  The strengths and weaknesses of high-throughput
approaches are then discussed in the context of perovskites, and in
particular for applications in photovoltaics. I will then show how
relatively simple approaches, either based on data-mining or machine
learning, can be used to speed up this process. These techniques turn
out to be rather intuitive, as they "learn" chemical rules by gaining
experience from a relatively small set of data. Finally, I speculate
which role machine learning will have in the future of materials
science.

Bio:
Miguel Marques received his PhD degree in Physics from the University of
Wuerzburg in 2000, working under the supervision of E.K.U. Gross in the field of density
functional theory for superconductors. He then held several
post-doctoral positions in Spain, Germany, and in France. From 2005 to
2007 he was assistant professor at the University of Coimbra in
Portugal. From 2007 to 2014 he was a CNRS researcher (CR1) at the
University of Lyon 1. Since then he is a professor at the Martin-Luther
University of Halle-Wittenberg. His current research interests include
density functional theory, superconductivity, application of machine
learning to materials science, etc. He authored 130 articles with more
than 9400 citations and a Hirsch index of 47 (source: Google Scholar),
and has edited three books published by Springer in their Lecture Notes
in Physics series. He also has organized several summer schools and
international workshops, the most relevant of which are the series of
the Benasque School and International Workshop in TDDFT, that takes
place in Benasque, Spain every second year.