Thursday, 14:00


HIDA Lectures @ DASHH

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Helmholtz Information & Data Science Academy Lectures

The HIDA Lectures is an event series organized by HIDA together with the six Helmholtz Information & Data Science Schools. Throughout the year, the Data Science Schools invite outstanding international Data Scientists to speak about their ongoing research.

As the Schools represent all Helmholtz research areas - Energy; Earth and Environment; Health; Information; Matter; and Aeronautics, Space and Transport - the series will reflect a wide range of topics and offers a great opportunity to dive into the diversity of current approaches in Data Science.

We cordially invite the interested public and especially PhD students in the Helmholtz Association, who can gain insight into the diverse activities of the Schools and HIDA but most importantly discuss with international researchers about different application fields of Data Science.

The host of the first HIDA Lecture is the "Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter". On April 15 at 2 pm, Anatole von Lilienfeld will take the virtual stage as our first speaker in the series.


HIDA Lectures @ DASHH

Quantum Machine Learning in Chemical Compound Space

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.

Mr. von Lilienfeld is Professor of Computational Materials Discovery at the University of Vienna. His research focus is highly interdisciplinary and uses physical, mathematical and computational sciences for the quantum mechanics based exploration of chemical space.

No registration required. To participate follow this link LINK.

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