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Webinar: From Project to Platform: New Resources on PSDI – Session 2

Webinar: From Project to Platform: New Resources on PSDI – Session 2

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.  

The recording of this webinar is now available on YouTube

Abstract

When TD-DFT Fails: BenchmarkSet1500, a Multireference Excited-State Dataset for Organic Semiconductor Discovery
Challenge
    • Accurate excited‑state prediction is critical for organic semiconductor design (e.g. OLEDs, OPVs)
    • Widely used single‑reference methods (e.g. TD‑DFT) often fail for: strong static correlation; double‑excitation character; inverted singlet–triplet gaps
    • Lack of reliable, large‑scale multireference benchmark data limits: method development; validation of excited‑state models; data‑driven and ML‑based discovery
Approach
    • Development of BenchmarkSet1500
      • a curated dataset of 1,500 organic molecules
      • excited‑state properties computed using multireference electronic structure methods
    • Systematic analysis of
      • molecular diversity
      • statistical distribution of excited‑state properties
    • Derivation of practical guidelines
      • selecting suitable levels of theory
      • based on molecular fragment type
    • Demonstration through targeted molecular screening
      • inverted singlet–triplet gaps
      • thermally activated delayed fluorescence (TADF)
      • deviations from Kasha’s rule
Innovation
    • First large‑scale multireference benchmark dataset focused on organic excited states
    • Enables quantitative assessment of TD‑DFT failure regimes
    • Provides a foundation for systematic excited‑state photophysics exploration
    • Supports method development, benchmarking, and validation beyond single‑reference models
    • Establishes a high‑quality data resource for future machine‑learning‑driven materials discovery

Bio

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.

Watch the recording

You can watch the recording of this webinar via our YouTube channel.
Slides are available on Zenodo: https://zenodo.org/records/20539210

The PSDI team looks forward to seeing you at the webinar, if you have any questions you can always get in contact with us.
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