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DTSTART:19700308T020000
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DTSTAMP:20260605T154543Z
LOCATION:Bldg. 6 - Room 004
DTSTART;TZID=Europe/Stockholm:20260630T151500
DTEND;TZID=Europe/Stockholm:20260630T154500
UID:submissions.pasc-conference.org_PASC26_sess143_msa236@linklings.com
SUMMARY:Data-Driven Prediction of Spin-Crossover Systems Using Electronic 
 Structure Calculations
DESCRIPTION:Jordi Cirera (University of Barcelona)\n\nSpin-crossover (SCO)
  systems exhibit reversible transitions between high-spin and low-spin sta
 tes, making them promising candidates for molecular switches, sensors, and
  memory devices. In this work, we present a data-driven framework that int
 egrates electronic structure calculations with statistical and machine lea
 rning approaches to predict spin-state energetics and transition behavior.
  Key descriptors derived from density functional theory (DFT), including l
 igand field strength, metal–ligand bond metrics, and frontier orbital char
 acteristics, are used to construct predictive models for spin-state splitt
 ing (ΔE_HL). By correlating these features with experimentally reported SC
 O properties, the approach enables rapid screening and rational design of 
 new candidates. This methodology highlights the potential of combining qua
 ntum chemical accuracy with data-driven insights to accelerate the discove
 ry of functional spin-crossover materials.\n\nDomain: Climate, Weather, an
 d Earth Sciences\n\nSession Chair: Marco Lattuada (Universiity of Fribourg
 )\n\n
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