BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Physical Sciences Data Infrastructure - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Physical Sciences Data Infrastructure 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:20260507T140000 DTEND;TZID=Atlantic/Azores:20260507T150000 DTSTAMP:20260502T134150 CREATED:20260416T102949Z LAST-MODIFIED:20260423T085945Z UID:37017-1778162400-1778166000@www.psdi.ac.uk SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 2 DESCRIPTION:Registration link: https://us06web.zoom.us/webinar/register/WN_uCSiENnvQpCUjLNERzvDFg \nPSDI 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.   \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 Challenge\n \n \n \n \n \n \n\n\nGrowing concerns of a reproducibility crisis in electrochemical devices\, particularly within the flow battery research community\nA lack of standardised experimental practices and few consistent reporting frameworks\nLimited reliability and comparability of reported results across laboratories\nInter‑lab differences are difficult to interpret\, slowing collective progress and best‑practice development\n\n\n\n \n \n \n \n \n Approach\n \n \n \n \n \n \n\n\nSince 2023\, multi‑institutional round‑robin studies co‑led by QUB and MIT\nSystematic investigation of repeatability\, replicability\, reproducibility in flow battery cell testing\nPhase 1 (complete)\n\nIdentical flow battery test cell kits distributed to 11 researchers from 7 institutions\nNominally identical electrochemical measurements performed\n\n\nPhase 2 (on-going)\n\nCommunity‑scale expansion to over 40 researchers from 35 institutions\nPhase 2a: reproducibility using participants’ own cells\nPhase 2b: large‑scale replicability study using updated standardised kits\n\n\n\n\n\n \n \n \n \n \n Innovation\n \n \n \n \n \n \n\n\nCombination of community‑scale participation\, shared nomenclature\, and affordable 3D-printed cells\nDevelopment of a PSDI‑supported data infrastructure for:\n\ncross‑institutional data collection\ninteractive visualisation\ncomparative analysis at scale\n\n\nEnables identification of systematic trends across laboratories\nEstablishes a pathway toward transparent\, comparable\, and reproducible testing standards for single‑cell flow batteries\n\n\n\n \n \n \n \n \n Phase 1: Replicability Study Timeline\n \n \n \n \n \n \n \n \n \n \n \n Phase 2: Community‑Scale Participation\n \n \n \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 Josh J. Bailey is an Illuminate Fellow at Queen’s University Belfast\, working at the interface of physical experimentation and computational modelling to improve performance\, durability\, and sustainability of electrochemical devices. He co-leads international activities aiming to measure and improve reproducibility in flow battery testing\, whilst designing new materials and protocols. \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 Register for this webinar\nRegister for this webinar directly through zoom:https://us06web.zoom.us/webinar/register/WN_uCSiENnvQpCUjLNERzvDFg \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-2/ LOCATION:Online\, Virtual Event\, Online CATEGORIES:Webinar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Atlantic/Azores:20260521T140000 DTEND;TZID=Atlantic/Azores:20260521T150000 DTSTAMP:20260502T134150 CREATED:20260417T151512Z LAST-MODIFIED:20260421T122659Z UID:37049-1779372000-1779375600@www.psdi.ac.uk SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 3 DESCRIPTION:Registration link: https://us06web.zoom.us/webinar/register/WN_El8J-MXqTii2ij81QZOxEw \nPSDI 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.   \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 Presentation 1\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 \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 Presentation 2\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n SimpNMR – a Tool for Ab initio-assisted analysis of NMR data of paramagnetic metal complexes in solution\n \n \n \n \n \n Challenge\n \n \n \n \n \n \n\n\nNMR spectra of paramagnetic metal complexes in solution are notoriously difficult to interpret\, as unpaired electrons produce large chemical shifts\, broadened lineshapes\, and temperature-dependent behaviour that standard diamagnetic analysis tools cannot handle.\nExtracting meaningful electronic structure information (magnetic susceptibility tensors\, correlation times\, spin-Hamiltonian parameters) from pNMR data requires combining experimental spectra with ab initio calculations\, a workflow that today remains fragmented\, manual\, and inaccessible to many researchers.\n\n\n\n \n \n \n \n \n Approach\n \n \n \n \n \n \n\n\nSimpNMR is a Python package that streamlines pNMR analysis by directly incorporating outputs of ab initio calculations.\nIt provides an end-to-end workflow for paramagnetic complexes in solution\, including:\n\nprediction of 1D NMR spectra (e.g. 1H\, 13C)\nassignment of experimental peaks to molecular sites\nfitting of the magnetic susceptibility tensor and correlation times to experimental pNMR data\n\n\nWith variable-temperature experiments\, SimpNMR extracts spin-Hamiltonian parameters such as the g-tensor\, and in certain cases the D-tensor\, directly from solution pNMR data\, information typically accessible only from EPR or SQUID magnetometry.\n\n\n\n \n \n \n \n \n Innovation\n \n \n \n \n \n \n\n\nSimpNMR transforms pNMR analysis from a bespoke\, expert-only procedure into a reproducible\, scriptable Python workflow that bridges computational and experimental chemistry.\nSimpNMR_DB\, a curated companion database\, stores the input data required for SimpNMR analysis\, enabling:\n\nreuse and benchmarking of ab initio inputs across complexes\nreproducible\, shareable analysis pipelines\naccelerated discovery by lowering the barrier to quantitative pNMR interpretation\n\n\nTogether\, SimpNMR and SimpNMR_DB open up solution pNMR as a practical route to electronic-structure parameters of paramagnetic metal complexes.\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 Dr Elizaveta A. Suturina (University of Bath) is a senior lecturer in Computational Chemistry at the University of Bath. Her research combines computational and experimental approaches to reveal key structural modifications that enhance magnetic properties in cobalt(II) complexes.She currently leads a project developing a Python toolkit for ab initio-assisted analysis of paramagnetic NMR of metal complexes\, which has inspired further research extending these approaches across different chemical systems. \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 Register for this webinar\nRegister for this webinar directly through zoom:https://us06web.zoom.us/webinar/register/WN_El8J-MXqTii2ij81QZOxEw \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