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DTSTAMP:20250822T115805Z
LOCATION:Room 5.2A17
DTSTART;TZID=Europe/Stockholm:20250618T093000
DTEND;TZID=Europe/Stockholm:20250618T100000
UID:submissions.pasc-conference.org_PASC25_sess148_msa201@linklings.com
SUMMARY:Machine Learning Analysis of Amorphous Materials: Decoding Disorde
 r Effects
DESCRIPTION:Ivan Lunati, Mattia Turchi, and Xuewei Zhang (Empa)\n\nAmorpho
 us materials, ubiquitous among natural and manufactured solids, exhibit to
 pological disorder and increased surface entropy. Atomic vacancies, in the
  form of undercoordinated defects, profoundly alter fluid/surface interact
 ions and can be exploited to design sorbents for direct air capture (DAC) 
 of carbon dioxide and sustainable catalysts.\nStandard atomistic modeling 
 and analysis techniques, however, are not tailored to amorphous solids, an
 d new tools and approaches need to be devised to characterize surface hete
 rogeneity. Machine learning (ML) provides powerful methods to address the 
 complexity of amorphous structures, enabling the identification of intrica
 te surface regions and relevant adsorption sites.\nWe apply segmentation t
 echniques to identify adsorptive structures with complex geometries that c
 annot be effectively analyzed using classical radial distribution function
 s. Additionally, we employ local atomic-environment descriptors to classif
 y surface structures and develop proxies and surrogate models to accelerat
 e the assessment and design of materials for energy and environmental appl
 ications.\n\nDomain: Chemistry and Materials\n\nSession Chair: Marco Lattu
 ada (Universiity of Fribourg)\n\n
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