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SUMMARY:"Machine learning in chemistry and beyond" (ChE-651) seminar by Mi
 guel Caro "Machine learning local atomic properties: accurate prediction o
 f XPS spectra of carbon materials"
DTSTART:20220510T151500
DTEND:20220510T161500
DTSTAMP:20260929T064703Z
UID:dcff936f8d859a2cdd24951be0f5669266f2bbd60dfc7c052644f979
CATEGORIES:Conferences - Seminars
DESCRIPTION:Miguel Caro is originally from a small town (Cartaya) in sout
 hwestern Spain. He graduated with a Physics degree from University of La L
 aguna\, Tenerife\, Spain. He then moved to Cork\, Ireland\, where he pursu
 ed a PhD in computational condensed-matter physics under Prof. Eoin O’Re
 illy at the Tyndall National Institute. His thesis work\, for which he was
  awarded his PhD in 2013\, focused on theory of III-N alloys\, a material 
 system widely used for optoelectronic applications. After the PhD\, he mov
 ed to Aalto University\, Finland as a postdoc in 2013. In 2017 he obtained
  the Academy of Finland Postdoctoral Researcher grant and since 2020 he is
  Academy of Finland Research Fellow. Dr. Caro’s current research interes
 ts concern the atomistic simulation of real materials\, especially carbon-
 based materials\, using a battery of simulation tools and methodologies\, 
 from density functional theory to machine learning.\nn recent years great 
 advances have been made in the development of machine learning based force
  fields. These force fields feed on reference DFT total energies and force
 s and\, once trained\, can make predictions from the knowledge of the atom
 ic positions alone. Less attention has been paid to predicting other atomi
 stic properties of materials\, such as spectroscopic features. Fortunately
 \, the same methodological and computational tools that have been develope
 d for force fields are very well suited to learn a variety of "local" atom
 ic properties. In this presentation I will talk about these local property
  models and discuss how we have employed them to learn adsorption energies
  [1]\, Hirshfeld volumes used to parametrize van der Waals corrections [2]
 \, and core-electron binding energies [3]. I will particularly focus on th
 e latter\, and show how these local models\, trained from a combination of
  multilevel reference data\, can be used to efficiently and accurately pre
 dict XPS spectra of complex materials.\n\n[1] M.A. Caro\, A. Aarva\, V.L.
  Deringer\, G. Csányi\, and T. Laurila. Chem. Mater. 30\, 7446 (2018).\n
 [2] H. Muhli\, X. Chen\, A.P. Bartók\, P. Hernández-León\, G. Csányi\
 , T. Ala-Nissila\, and M.A. Caro. Phys. Rev. B 104\, 054106 (2021).\n[3]
  D. Golze\, M. Hirvensalo\, P. Hernández-León\, A. Aarva\, J. Etula\, T
 . Susi\, P. Rinke\, T. Laurila\, and M.A. Caro. arXiv preprint arXiv:2112
 .06551.
LOCATION:https://epfl.zoom.us/j/64473017589?pwd=Vmpnd1pleGhEb1hFb3kxUlNIUW
 JyQT09
STATUS:CONFIRMED
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