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DTSTART;TZID=Europe/Stockholm:20230626T170000
DTEND;TZID=Europe/Stockholm:20230626T173000
UID:submissions.pasc-conference.org_PASC23_sess117_msa281@linklings.com
SUMMARY:A Machine-Learning Based Hierarchical Framework to Discover Novel 
 Functional Materials
DESCRIPTION:Minisymposium\n\nAnjana Talapatra (Los Alamos National Laborat
 ory)\n\nThe compositional and structural variety inherent to oxide perovsk
 ites and their fascinating properties spawn wide-ranging applications from
  electromechanical devices to opto-electronic materials for radiation dete
 ction. The band gap in these materials can be optimally controlled by vary
 ing the composition. Here, we use a novel hierarchical screening process, 
 wherein we build four machine learning (ML) models, designed to be applied
  sequentially to a very large chemical space, to yield novel double oxide 
 perovskite chemistries that are predicted to be experimentally formable, t
 hermodynamically stable and are insulator materials with a significant ban
 d gap. We identify a tractable set of promising candidates with high confi
 dence and computationally verify their stability and band gaps . Our multi
 -step hierarchical screening approach, which may be generalized to investi
 gate other classes of materials in addition to the oxide perovskites exami
 ned here, provides further impetus to the application of physics-based ML 
 models to the discovery of novel functional materials.\n\nDomain: Chemistr
 y and Materials\n\nSession Chair: Pascal Boulet (Aix-Marseille University)
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