BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Physical Sciences Data Infrastructure - ECPv6.16.4.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.psdi.ac.uk
X-WR-CALDESC:Events for Physical Sciences Data Infrastructure
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Atlantic/Azores
BEGIN:DAYLIGHT
TZOFFSETFROM:-0100
TZOFFSETTO:+0000
TZNAME:+00
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:-0100
TZNAME:-01
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0100
TZOFFSETTO:+0000
TZNAME:+00
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:-0100
TZNAME:-01
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0100
TZOFFSETTO:+0000
TZNAME:+00
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:-0100
TZNAME:-01
DTSTART:20271031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Atlantic/Azores:20260521T140000
DTEND;TZID=Atlantic/Azores:20260521T150000
DTSTAMP:20260604T092733Z
CREATED:20260417T151512Z
LAST-MODIFIED:20260604T092733Z
UID:37049-1779372000-1779375600@www.psdi.ac.uk
SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 2
DESCRIPTION:PSDI is pleased to launch a new webinar series entitled “From Project to Platform: New Resources on PSDI”. This series aims to showcase the high-quality tools and resources developed through the funding call 2025\, introduce them to a broader community\, and foster engagement with relevant user groups.   \nThe recording of this webinar is now available on YouTube \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Abstract\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				When TD-DFT Fails: BenchmarkSet1500\, a Multireference Excited-State Dataset for Organic Semiconductor Discovery\n			\n				\n				\n				\n				\n				Challenge\n			\n				\n				\n				\n				\n				\n\n\nAccurate excited‑state prediction is critical for organic semiconductor design (e.g. OLEDs\, OPVs)\nWidely used single‑reference methods (e.g. TD‑DFT) often fail for: strong static correlation; double‑excitation character; inverted singlet–triplet gaps\nLack of reliable\, large‑scale multireference benchmark data limits: method development; validation of excited‑state models; data‑driven and ML‑based discovery\n\n\n\n			\n				\n				\n				\n				\n				Approach\n			\n				\n				\n				\n				\n				\n\n\nDevelopment of BenchmarkSet1500\n\na curated dataset of 1\,500 organic molecules\nexcited‑state properties computed using multireference electronic structure methods\n\n\nSystematic analysis of\n\nmolecular diversity\nstatistical distribution of excited‑state properties\n\n\nDerivation of practical guidelines\n\nselecting suitable levels of theory\nbased on molecular fragment type\n\n\nDemonstration through targeted molecular screening\n\ninverted singlet–triplet gaps\nthermally activated delayed fluorescence (TADF)\ndeviations from Kasha’s rule\n\n\n\n\n\n			\n				\n				\n				\n				\n				Innovation\n			\n				\n				\n				\n				\n				\n\n\nFirst large‑scale multireference benchmark dataset focused on organic excited states\nEnables quantitative assessment of TD‑DFT failure regimes\nProvides a foundation for systematic excited‑state photophysics exploration\nSupports method development\, benchmarking\, and validation beyond single‑reference models\nEstablishes a high‑quality data resource for future machine‑learning‑driven materials discovery\n\n\n\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Bio\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Malin Zollner (University of Strathclyde) is a Research Assistant in Chemistry at the University of Strathclyde\, funded by PSDI. Her work focuses on developing data resources to support organic semiconductor discovery\, with applications in data-driven modelling and machine learning.She completed her MChem in Pure and Applied Chemistry at the University of Strathclyde in 2024\, where she began exploring the intersection of computational chemistry and materials discovery\, and has since developed a strong background in machine learning for chemical applications. \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Watch the recording\nYou can watch the recording of this webinar via our YouTube channel.Slides are available on Zenodo: https://zenodo.org/records/20539210 \n			\n				\n				\n				\n				\n				The PSDI team looks forward to seeing you at the webinar\, if you have any questions you can always get in contact with us.
URL:https://www.psdi.ac.uk/event/new-resources-webinar-3/
LOCATION:Online\, Virtual Event\, Online
CATEGORIES:Webinar
END:VEVENT
END:VCALENDAR