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DTSTAMP:20230831T095746Z
LOCATION:Schwarzhorn
DTSTART;TZID=Europe/Stockholm:20230627T163000
DTEND;TZID=Europe/Stockholm:20230627T170000
UID:submissions.pasc-conference.org_PASC23_sess128_msa119@linklings.com
SUMMARY:Combining the Power of High-Performance Computing, High-Throughput
  Ab Initio Calculations, and Machine Learning towards Materials Informatic
 s
DESCRIPTION:Minisymposium\n\nGian-Marco Rignanese (Université catholique d
 e Louvain)\n\nThe progress in first-principles simulation codes and superc
 omputing capabilities have given birth to the so-called high-throughput (H
 T) ab initio approach, thus allowing for the identification of many new co
 mpounds for a variety of applications (e.g. lithium battery and photovolta
 ic). As a result, a number of databases have also become available online,
  providing access to various properties of materials, mainly ground‑state 
 though. Indeed, for more complex properties (e.g., linear or higher‑order 
 responses), the HT approach is still out of reach because of the required 
 CPU time. To overcome this limitation, machine learning approaches have re
 cently attracted much attention in the framework of materials design. In t
 his talk, I will review recent progress in the emerging field of materials
  informatics, high‑throughput ab initio calculations and machine learning.
  I will briefly introduce the OPTIMADE API [*] that was developed for sear
 ching the leading materials databases (such as AFLOW, the Materials Cloud,
  the Materials Project, NOMAD, OQMD, ...) using the same queries. I will i
 llustrate the power of materials informatics through a few recent examples
 .<br />[*] C.W. Andersen <em>et al.</em>, Sci. Data 8, 217 (2021).\n\nDoma
 in: Chemistry and Materials\n\nSession Chair: Fabio Affinito (CINECA)
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