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:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20241027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20271031T010000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20271031T010000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20271031T010000 END:STANDARD END:VTIMEZONE 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=Europe/London:20251211T140000 DTEND;TZID=Europe/London:20251211T150000 DTSTAMP:20260422T210458 CREATED:20251120T101526Z LAST-MODIFIED:20251219T164610Z UID:36548-1765461600-1765465200@www.psdi.ac.uk SUMMARY:Webinar: How do we use metadata to plug your Physical Science data or software into PSDI? DESCRIPTION:This webinar introduces the first version of the PSDI launched in spring 2025\, explaining how to integrate physical sciences resources like data\, services\, tools\, and guidance into its resource catalogue and cross-data search using PSDI’s metadata framework. \nThe recording of this webinar is now available on YouTube \n \n \n \n \n \n Abstract\n​The first version of the Physical Sciences Data Infrastructure (PSDI) was launched in spring this year. Here we describe how we plug a physical sciences resource (data\, services\, tools\, guidance) into PSDI’s resource catalogue and PSDI cross data search via PSDI’s metadata. This webinar would be interest to new PSDI partners and physical sciences researchers who are considering contributing to PSDI in the future. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Biography\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n   \nDr Aileen Day is a Senior Data Engineer for the Physical Sciences Data Infrastructure (PSDI)\, where she leads the development of PSDI’s metadata. Throughout her career\, she has bridged science (Chemistry and Materials Science) and computing (computer modelling\, programming\, and databases). She began by studying materials science at Cambridge University\, followed by a PhD in the chemistry department at University College London\, focusing on computer modelling of zeolites. She then served as a Materials Information Consultant for Granta Design\, creating databases of materials properties\, test data\, and design data\, while training customers on setup\, usage\, and workflow integration. Subsequently\, she worked for the Royal Society of Chemistry\, developing RSC resources (including RSC Publications\, ChemSpider\, ontologies\, and educational projects like the RSC Learn Chemistry Wiki) and linking them internally and externally. \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. \n\n\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/webinar-metadata/ LOCATION:Online\, Virtual Event\, Online CATEGORIES:Webinar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20260108T140000 DTEND;TZID=Europe/London:20260108T150000 DTSTAMP:20260422T210458 CREATED:20251120T104207Z LAST-MODIFIED:20260129T110512Z UID:36554-1767880800-1767884400@www.psdi.ac.uk SUMMARY:Webinar: Trusted and reproducible workflows for machine learnt interatomic potentials DESCRIPTION:This webinar explores recent advances in machine learnt interatomic potentials (MLIPs) that revolutionize atomistic simulations with ab initio accuracy and expanded scales\, while introducing software frameworks such as janus-core\, aiida-mlip\, and ML-PEG for data generation\, benchmarking\, training\, and workflow integration within the PSDI ecosystem. \nThe recording of this webinar is now available on YouTube \n \n \n \n \n \n Abstract\nRecent advances in machine learnt interatomic potentials (MLIPs) are revolutionising atomistic simulations\, enabling atomistic modelling with accuracy comparable to ab initio calculations extending significantly time and length scales. However\, in order for researchers to be able to take full advantage of these advances\, software frameworks are needed to facilitate data generation\, scientific benchmarking\, training and fine-tuning of MLIPs\, as well as to enable their integration into simulation workflows to study properties of interest. To address this need\, we introduce (a) janus-core\, (b) aiida-mlip and (c) ML-PEG. \nThe main focus of this highlight will be aiida-mlip and ML-PEG. aiida-mlip is an AiiDA plugin\, enabling full provenance tracking and HPC integration for workflows involving MLIPs\, such as high-throughput calculations and fine-tuning workflows. ML-PEG is an ML potential usability and performance guide\, providing a framework to develop\, run\, and visualise an automated\, modular\, hierarchical test suite for MLIPs. ML-PEG is highly interactive\, with users able to explore the results of tests at multiple levels of detail\, and customise the relative importance and scaling of individual tests according to their applications and properties of interest. How these integrate in the larger ecosystem of PSDI\, like data collections would also be highlighted. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Biography\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 Elliott Kasoar is a research software engineer in the data-driven materials and molecular science group within STFC’s scientific computing department. As part of the PSDI Data to Knowledge pathfinder\, he leads the development of digital infrastructure for machine learnt interatomic potentials. \nHe is also pursuing a part-time PhD in Gábor Csányi’s group at the University of Cambridge\, with a current focus on developing an ML potential usability and performance guide as both a deployed service and deployable software framework. \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 \n \n \n \n \n \n Dr Alin-Marin Elena is a computational scientist at STFC Daresbury Laboratory\, specializing in computational statistical physics\, molecular dynamics and Monte Carlo methods. He contributes to open-source scientific software\, including CP2K\, DL_POLY (5.0)\, ASE\, and janus-core\, and leads the Data-Driven Molecular and Materials Sciences group at STFC. With a keen interest in machine learnt interatomic potentials—their generation\, usage\, and application to explain experimental results—as well as computational statistical mechanics of rare events\, HPC\, continuous integration and deployment\, and user experience for scientific codes\, he leads the Data to Knowledge pathfinder in PSDI. He earned his PhD in Physics from University College Dublin in 2013 under the supervision of Prof Giovanni Ciccotti and Dr Simone Meloni. Prior to and following his PhD\, he worked as a computational scientist at the Irish Centre for High End Computing\, where he coordinated the National Service and participated in the Intel Parallel Computing Centre program for code modernization on emerging architectures. \nHe is a member of the Computational Molecular and Materials Science Theme at STFC and is involved in EPSRC/MRC/BBSRC-funded CoSeC initiatives for exchanging computational knowledge and expertise through training and outreach for CoSeC\, PSDI\, and the Ada Lovelace Center. Notably\, he serves on the organizing committee of the CCP5 Summer School (co-sponsored by CECAM) and the CaMML school (co-sponsored by PSDI and AiHUB). \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. \n\n\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/webinar-data-knowledge-pathfinder/ LOCATION:Online\, Virtual Event\, Online CATEGORIES:Webinar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Paris:20260216T130000 DTEND;TZID=Europe/Paris:20260216T160000 DTSTAMP:20260422T210458 CREATED:20260112T110155Z LAST-MODIFIED:20260113T155832Z UID:36638-1771246800-1771257600@www.psdi.ac.uk SUMMARY:IDCC26: Workshop 7 - Evolving data management plans DESCRIPTION:📅 Date: 16 February 2026📍 Venue: Esplanade Zagreb Hotel\, Croatia💷 Fee: Free (registration required)👉 Register: Click here \n \n \n \n \n \n Workshop Description\nEffective management of research data including methods\, workflows\, and code is essential for ensuring research quality\, ethical practice\, and the FAIR principles that enable long‑term reuse. Although the Data Management Plan (DMP) is the primary instrument intended to support these aims\, its current form-based implementation often functions as a compliance requirement rather than a mechanism that meaningfully improves practice. We think that DMPs should be much more than that\, not just serving as an active\, dynamically updated record of the research project but also including smart functionality and integrating with the other tools we use to manage our research. \nIn this half-day workshop\, held as part of IDDC26\, we aim to bring together a diverse group of research professionals including data stewards\, librarians\, research technicians and repository managers to facilitate knowledge exchange and collaborative idea generation around current and future enhancements to the functionality and utility of DMPs.   \nWe invite participants to contribute a lightning talk highlighting revolutionary or highly effective tools and approaches they have used or developed for data management planning or research data management. This is a great opportunity to share practical experiences and inspire others during the session. \nTarget Audience\nThis workshop is intended for anyone working in scientific research or involved in planning\, managing\, documenting\, curating\, and sharing research outputs\, including: \n\nResearchers in all disciplines and at all career stages\nData stewards\, data managers\, and other research support professionals\nData librarians\nResearch technicians\nResearch software developers\nRepository managers and other data curation professionals\nResearch funders and policymakers\nOther research data professionals\n\nDraft Agenda\nThis is a draft agenda for the workshop\, timings might changed based on the number of lightning talks submitted. \n\n13:00 – 13:15: Introductions ​\n13:15 – 13:35: Introduction and Background to PSDI​\n13:35 – 14:25: Focus Group Activity: What do we need DMPsto do?​\n14:25 – 14:35: Talks: Effective tools and new developments​\n14:35 – 14:50: Coffee Break​\n14:50 – 15:00: Talks: Smart\, Integrated\, Active\, Useful!​\n15:00 –15:40: Focus Group Activity: Visions for the future​\n15:40–15:55: Feedback from Focus Groups & GroupDiscussion​\n15:55–16:00: Wrap up and Next Steps​\n\n \n Register URL:https://www.psdi.ac.uk/event/idcc26-workshop-7-evolving-data-management-plans/ LOCATION:Esplanade Zagreb Hotel\, 1 Mihanovićeva ulica\, Zagreb\, Croatia (Local Name: Hrvatska) CATEGORIES:Workshop ATTACH;FMTTYPE=image/png:https://www.psdi.ac.uk/wp-content/uploads/2026/01/Workshop-7.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20260226T140000 DTEND;TZID=Europe/London:20260226T150000 DTSTAMP:20260422T210458 CREATED:20260114T153125Z LAST-MODIFIED:20260311T141225Z UID:36658-1772114400-1772118000@www.psdi.ac.uk SUMMARY:Webinar: Breaking Data Silos - From static documents to living data DESCRIPTION:This webinar demonstrates how Data Revival applies cutting edge computer vision and domain specific AI to unlock chemical “dark data” trapped in documents\, patents\, and historical notebooks\, transforming it into structured\, interoperable\, AI ready assets that accelerate discovery and dissolve traditional data silos. \nThe recording of this webinar is now available on YouTube \n \n \n \n \n \n Abstract\nIn the physical sciences\, critical R&D knowledge remains trapped in a vast array of static formats—ranging from handwritten lab notebooks and internal company reports to academic literature and patent filings. This fragmentation creates data silos that hinder innovation and prevent the deployment of modern digital tools. \nIn this webinar\, Data Revival will demonstrate how advanced computer vision and domain-specific AI can transform this “dark data” into living\, interoperable assets. We will showcase our specialized Optical Chemical Structure Recognition (OCSR) technology\, which is uniquely capable of interpreting the full spectrum of chemical representations: standard printed molecules\, complex Markush structures found in intellectual property\, and hand-drawn structures from legacy notebooks. \nAttendees will see this technology applied in real-world scenarios\, including the creation of comprehensive reaction databases from thousands of Organic Process Research & Development (OPRD) papers \, the systematic parsing of patent landscapes\, and the automated extraction of historical notebook data to populate modern Electronic Lab Notebooks (ELNs). Join us to learn how we bridge the gap between static archives and AI-ready infrastructure. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Biography\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 Samuel Munday is the CEO and co-founder of the University of Southampton spin out Data Revival. He first became interested in scientific data management whilst building a predictive analytics platform for polymeric materials and realising that a lot of key data resided in a form incomprehensible to computers. This has led to the development of a series of tools for unstructured chemical data extraction and structuring\, mainly used for turning hand written lab notebooks\, patents and academic literature into structured searchable databases at scale. These services are now being used by large multinational chemical and pharmaceutical companies in areas as diverse as semiconductors\, polymers and drug 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 Watch the recording\nYou can watch the recording of this webinar via our YouTube channel. Slides are available on Zenodo. \n\n\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/webinar-data-revival-2/ LOCATION:Online\, Virtual Event\, Online CATEGORIES:Webinar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20260312T130000 DTEND;TZID=Europe/London:20260312T163000 DTSTAMP:20260422T210458 CREATED:20260126T125724Z LAST-MODIFIED:20260319T113611Z UID:36699-1773320400-1773333000@www.psdi.ac.uk SUMMARY:Online Meeting on Electronic Research Notebooks: Implementation & Adoption Success Stories DESCRIPTION:The recording of this webinar is now available on YouTube​ \n \n \n \n \n \n Event Description\nAre you considering implementing an Electronic Research Notebook (ERN) for your institution or research group but aren’t sure where to start? Have you perhaps already tried to implement an ERN and were unable to overcome certain obstacles? Or are you a digital master who has successfully implemented one and want to share your experiences? Join us for a thought provoking online meeting to share knowledge around the practicalities\, benefits\, and success stories of ERN adoption. \n \n \n \n \n \n Event Audience\nThis event is designed for anyone interested in digitising research workflows\, moving from paper notebooks (lab or otherwise!) to digital solutions\, and ensuring well-documented research data. Learn from real-world examples\, discover different ERN options\, and gain insight into the challenges and considerations required for successful ERN implementation.  \n \n \n \n \n \n Agenda\n\n13:00 – 13:15: Introductions to the Community\n13:15 – 14:15: Electronic Research Notebook Experiences\n\nOneNote Portfolios for Supporting Laboratory and Research Skills Development in Undergraduate Students – Chloe Harold and Chris Hawes (Keele University)Digital portfolios are increasingly used to support reflective and authentic assessment in higher education. This talk describes the use of Microsoft OneNote as a platform for laboratory portfolios in our undergraduate chemistry course. We discuss the rationale for adopting a portfolio-based assessment model\, outline the practical implementation of OneNote portfolios in laboratory courses across all levels\, and evaluate the strengths and limitations of OneNote for portfolio use. Student and staff feedback highlights improved organisation\, reflection\, and contemporaneous engagement with laboratory learning\, alongside improved module outcomes. We conclude by demonstrating how this approach can be transferred beyond laboratory assessment and adapted for use in other disciplines.\nUsing a general note-taking software as a flexible ERN – Dr. Danny Garside (Digital Research Academy)When Danny was a postdoc in a neuroscience lab at the National Institutes of Health in Washington DC they were tasked with finding a replacement to the lab’s system of paper notebooks. They settled on logseq – a general note-taking software which is open-source and flexible. They will discuss the reasons for this choice (free\, flexible\, no vendor lock-in\, supporting the development of open-source tools)\, how they implemented it (see this blog post)\, and lessons learnt along the way.\nImplementing OneNote in Chemistry Undergraduate Labs – Dr Philip Leadbitter (University of Southampton) In late 2019\, the undergraduate teaching laboratories in Chemistry and Chemical Engineering at the University of Southampton (UoS) underwent a major refurbishment\, including the introduction of teaching laptops to the labs. This paved the way for the teaching labs to phase out physical notebooks. Yet this phasing out process was not without its complications\, and it was not until 2024 a comprehensive replacement for the old physical notebooks was fully implemented. This talk will share insights from our implementation of OneNote and explain why ultimately a fully fledged ELN is now considered more suitable for our needs.\nTrialing and Implementing Revvity Signals in Chemistry Research Labs – Dr Samantha Pearman-Kanza (University of Southampton)In 2025\, the University of Southampton trialled the Revvity Signals Electronic Lab Notebook (ELN) across 12 chemistry research groups\, engaging 36 researchers and capturing over 120 experiments per week. This presentation shares key lessons from the pilot\, highlighting benefits such as improved workflow consistency\, ChemDraw integration\, and embedded Health & Safety documentation\, alongside technical and adoption challenges that emerged. The talk explores critical success factors for ELN implementation\, including stakeholder engagement and user support\, and outlines considerations for optimising and scaling ELN use across academic research environments.\nAI4Green: an open-source ELN promoting sustainability chemistry – Professor Jonathan Hirst (University of Nottingham)Digital tools will be a critical part of making chemistry research laboratories more sustainable. Our AI4Green open-source electronic laboratory notebook (ELN)\, https://ai4green.app\, combines features including data archival and collaboration tools. The application’s design facilitates the integration of auxiliary sustainability applications. For example\, the open-source retrosynthesis software\, AiZynthFinder has been integrated into the platform. AI4Green features a sustainable solvent selection tool\, which comprises the Solvent Guide and the Solvent Surfer. The latter is an interactive principal component analysis (PCA) that provides users with an easy method to determine greener solvent alternatives.\nImplementing RSpace as an institutional Electronic Research Notebook for UCL – James A J Wilson (UCL)After a couple of years of discussing and measuring the potential need for an institutional ERN/ELN for University College London\, a decision was made in 2020 that the time was right to acquire a system that would benefit a broad cross-section of the university and the university went to tender to purchase and implement such a system. We settled on RSpace\, as the best fit to our strategy and after user testing. RSpace has now been in place for five years – enough time to learn lessons about what has worked and what remains to be done. This presentation will summarize the story behind the selection of RSpace and what we have learnt on the way.\n\n\n14:15 – 14:45: Q+A Panel with Speakers  \n14:45 – 15:00: Coffee Break\n15:00 – 16:30: Interactive Discussion Session\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Speaker Details\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 Chloe Harold (Keele University)Chloe Harold is a Chemistry lecturer at Keele University with 17 years of teaching experience. Three years ago\, she introduced OneNote laboratory portfolios into the first-year chemistry laboratory module in response to the limitations of traditional hard-backed lab diaries. Since then\, she has supported colleagues at Keele and at other universities in adopting digital laboratory portfolios. \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 \n \n \n \n \n \n Dr Chris Hawes (Keele University)Chris Hawes is a lecturer and joint programme director of the Chemistry undergraduate programmes at Keele University\, with background as a structural inorganic chemistry researcher. As module lead of Keele’s year 2 laboratory module and year 4 MChem research project module\, he has followed Chloe’s successful year 1 pilot to help expand OneNote laboratory and research notebooks to the remainder of our Chemistry undergraduate programme as part of Keele’s recent curriculum redesign. \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 \n \n \n \n \n \n Dr. Danny Garside (Digital Research Academy)Danny Garside is a neuroscientist and meta-scientist\, who splits their time between researching colour vision and trying to make academia more accessible\, more efficient\, and happier. They are the Community Manager for the Digital Research Academy\, and currently excited about the opportunities for academic research co-operatives. \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 \n \n \n \n \n \n Dr Philip Leadbitter (University of Southampton)Dr Philip Leadbitter is a research fellow at the University of Southampton\, working for the Physical Sciences Data Infrastructure (PSDI). His broad focus is on teaching\, both developing training and more relevant here process recording studies focused on undergraduate teaching laboratories. Recently he has working with the University teaching staff to successfully implement OneNote as a electronic lab notebook\, paving the way for a higher quality of teaching for students in the coming years. \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 \n \n \n \n \n \n Dr Samantha Pearman-Kanza (University of Southampton)Dr. Samantha Pearman-Kanza is a Principal Enterprise Fellow at the University of Southampton\, the Principal Investigator for the Careers and Skills for Data-driven Research Network (CaSDaR)\, Co-Investigator  for the Physical Sciences Data Infrastructure (PSDI) Initiative\, and a researcher for the AI in Chemistry Hub (AIChemy). Samantha sits on the Advisory Boards for the Future Labs Live (Basel) and London Labs Live (UK) Conferences\, the Machines Learning Chemistry Project (University of Nottingham)\, the STEP-UP project (Imperial College London)\, and the Knowledger Project (University of North Florida)\, and the UK electronic information Group (UKeiG) STRIX Committee. She is also the Faculty Deputy Chair of the Ethics Committee. Samantha’s key research areas are ELNs\, process recording\, FAIR data\, data stewardship and research data management\, and semantic web technologies. \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 \n \n \n \n \n \n Professor Jonathan Hirst (University of Nottingham)Jonathan Hirstis Professor in Computational Chemistry at the University of Nottingham. In 2020\, he was awarded a Chair in Emerging Technologies by the Royal Academy of Engineering\, focusing on research that will empower the development of next-generation molecules that chemical engineers and chemists make\, by using machine learning to augment human decision-making. His tenure as Head of School (2013-2017) saw some significant transformations under his leadership\, including the building of the GSK Carbon Neutral Laboratory and a successful bid for an Athena Swan Silver Award. \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 \n \n \n \n \n \n James A J WilsonDr James A J Wilson is Head of Research Data Services at the Centre for Advanced Research Computing (ARC) at UCL He has led the development of Research Data Stewardship as a profession in UCL\, building a team of eighteen research data stewards who run data management services and collaborate with researchers to support good data management and ensure data is as FAIR as possible. In 2020\, James led the implementation of an institutional Electronic Research Notebook at UCL\, based on RSpace\, and runs the ERN User Group. He is an active member of the Research Data Alliance and a co-chair of the Research Data Architectures for Research Institutions (RDARI) Interest Group. URL:https://www.psdi.ac.uk/event/electronic-research-notebooks/ LOCATION:Online\, Virtual Event\, Online CATEGORIES:Webinar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20260330T100000 DTEND;TZID=Europe/London:20260330T160000 DTSTAMP:20260422T210458 CREATED:20260303T162325Z LAST-MODIFIED:20260304T154219Z UID:36783-1774864800-1774886400@www.psdi.ac.uk SUMMARY:Workshop: NMR Data Analysis of Paramagnetic Metal Complexes DESCRIPTION:As part of PSDI’s 2025 funding call\, project partners at the University of Bath (led by Dr Elizaveta Suturina) are hosting a one-day workshop focused on the NMR data analysis of paramagnetic metal complexes in solution\, supported by quantum chemistry calculations. \nThis event is supported by PSDI and is free to attend\, with lunch and refreshments provided. \n \n \n \n \n \n Event Details\n📅Date: Monday\, 30 March🕘Time: 10:00 am – 4:00 pm📍Location: 1 South 0.01\, Department of Chemistry\, University of Bath \n \n \n \n \n \n About the Workshop\nThis workshop will explore both experimental and computational approaches to paramagnetic NMR (pNMR)\, providing participants with practical tools and expert insights. \nMorning Session – Invited Speakers\nThe morning will feature talks from: \n\n Dr. Markus Enders (Universität Heidelberg)\n Lucas Lang (Technische Universität Berlin)\n\nSpeakers will cover advanced methods for analysing paramagnetic NMR data and integrating quantum chemical calculations to support structural interpretation. \nAfternoon Session – Hands-On Training\nThe afternoon will include a practical session using SimpNMR software\, along with a “bring your own research” segment where participants can receive direct support in analysing their own pNMR data. \nParticipants attending the afternoon session should bring their own laptops. \n \n \n \n \n \n Who Should Attend?\nThis workshop is aimed at PhD students and researchers who: \n\nWork with paramagnetic metal complexes\nHave experience measuring pNMR and/or calculating NMR/EPR parameters ab initio\n\n \n \n \n \n \n Registration Details\nRegistration for this event is required and places are limited. To secure a place\, please complete the Expression of Interest form. \nFor specific enquiries\, please contact Dr Elizaveta Suturina at e.suturina@bath.ac.uk. URL:https://www.psdi.ac.uk/event/nmr_workshop_bath/ LOCATION:Private: University of Bath CATEGORIES:Workshop END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20260413 DTEND;VALUE=DATE:20260418 DTSTAMP:20260422T210458 CREATED:20251024T100349Z LAST-MODIFIED:20251024T100532Z UID:36525-1776038400-1776470399@www.psdi.ac.uk SUMMARY:Chemical and Materials Machine Learning School 2026 DESCRIPTION:📅 Dates: 13–17 April 2026📍 Venue: STFC Daresbury Laboratory\, United Kingdom💷 Fee: £250 (includes 4 nights’ accommodation & catering)👉 Website / Apply here: spring2026.camml.ac.uk \n \n \n \n \n \n Overview\n \n \n \n \n \n The Chemical and Materials Machine Learning School (CaMMLs) is a five-day intensive training course designed for PhD students (and a limited number of industrial applicants) working in the field of materials and molecular simulations who have coding experience but are not yet highly experienced with machine learning (ML). The school is organised by Physical Sciences Data Infrastructure (PSDI) in collaboration with AIchemy\, and supported by STFC‑SCD\, CCP5 and CCP9. \nParticipants will explore the latest ML methods for atomistic simulation of materials and molecules through a combination of talks\, hands-on practical sessions and poster presentations. Topics include fundamentals of machine learning\, interatomic potentials and graph neural networks. \n \n \n \n \n \n Learning Outcomes\n \n \n \n \n \n By the end of the school\, participants will: \n\n\nGain awareness of state-of-the-art ML methods for atomistic and molecular simulations \n\n\nGain practical experience applying ML techniques in real-world research contexts \n\n\n \n \n \n \n \n Key Dates\n \n \n \n \n \n \n\nApplication deadline: 26 November 2025 \n\n\nNotification of acceptance: 17 December 2025 \n\n\nPayment deadline: 13 February 2026 \n\n\n \n \n \n \n \n Who Should Attend\n \n \n \n \n \n This school is aimed primarily at PhD students in materials & molecular simulation who already code but are new to machine learning. A limited number of places may be available for industrial applicants. Places are limited and\, in the event of oversubscription\, we will prioritise a diverse cohort of participants. \n \n \n \n \n \n How to Apply\n \n \n \n \n \n Visit spring2026.camml.ac.uk to complete your application. Payment of the course fee must be made by 13 February 2026 upon acceptance. Accommodation and catering for four nights are included in the fee. \n \n \n \n \n \n Contact / Further Information\n \n \n \n \n \n For any enquiries please contact Alin M Elena at alin-marin.elena@stfc.ac.uk. We encourage you to share this opportunity with colleagues and students who may be interested. URL:https://www.psdi.ac.uk/event/cammls-2025-2/ LOCATION:Daresbury Laboratory\, Keckwick Lane\, Daresbury\, WA4 4AD\, United Kingdom CATEGORIES:Training END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Paris:20260423T140000 DTEND;TZID=Europe/Paris:20260423T150000 DTSTAMP:20260422T210458 CREATED:20260310T164302Z LAST-MODIFIED:20260327T105757Z UID:36874-1776952800-1776956400@www.psdi.ac.uk SUMMARY:Webinar: BioSimDR - A Collection of Data Tools and Infrastructure for Biomolecular Simulation DESCRIPTION:Register here: https://us06web.zoom.us/webinar/register/WN_194clTg1QAqPIVILzXsTBg \nThis webinar illustrates how BioSimDR transforms scattered biomolecular simulation data into interoperable\, provenance-rich resources for broader reuse. \n \n \n \n \n \n Abstract\nBiomolecular simulations generate rich\, atomic-level insights into the dynamics of complex biological systems\, but sharing\, interpreting and reusing these datasets remains challenging. BioSimDR (BioSim Data Resources) is a PSDI-funded initiative that works with the CCPBioSim and HECBioSim communities to bring FAIR principles to biomolecular simulation data. \nIn this webinar\, we will outline common barriers to simulation reproducibility\, including inaccessible protocols\, missing metadata\, and incomplete records of simulation steps\, and we will introduce the BioSimDR tools designed to address these challenges. We will demonstrate BioSimDB\, a prototype data repository tailored for biomolecular simulations\, and new provenance-capture tools that allow researchers to automatically record every simulation step for easier sharing and reuse. \nAttendees will learn: \n\n\n\nHow provenance capture supports reproducible and reusable MD simulations\nHow BioSimDB enables standardised storage\, discovery\, and sharing of biomolecular simulation datasets\nHow the BioSimDR initiative collaborates with the community to build consensus-driven standards for FAIR simulation data\n\n\n\nThis session is intended for researchers generating\, analysing\, or reusing biomolecular simulations who want to improve transparency and reproducibility in their workflows. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Biography\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 Jas Kalayan from STFC is a scientific software engineer specialising in reproducible workflow development and data‑sharing solutions for molecular simulation. She has a strong research background in advanced molecular modelling\, including the development of machine‑learned interatomic potentials and entropy‑based methodologies. Her work has supported molecular dynamics studies focusing on protein–ligand binding\, hydration phenomena\, and free‑energy calculations\, with an overarching goal of improving transparency and reproducibility in computational biomolecular science. \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_194clTg1QAqPIVILzXsTBg \n\n\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/webinar-biosimdr/ LOCATION:Online\, Virtual Event\, Online CATEGORIES:Webinar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Atlantic/Azores:20260430T150000 DTEND;TZID=Atlantic/Azores:20260430T160000 DTSTAMP:20260422T210458 CREATED:20260401T100337Z LAST-MODIFIED:20260420T103423Z UID:36980-1777561200-1777564800@www.psdi.ac.uk SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 1 DESCRIPTION:Registration link: https://us06web.zoom.us/webinar/register/WN_kaav0loGQp24L4VDOEPuuA \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 \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 “Universal” Hyper-Active Learning for Machine Learning Interatomic Potentials\n \n \n \n \n \n Challenge\n \n \n \n \n \n \n\n\nBuilding accurate machine‑learning models of atomic interactions requires carefully curated training datasets\, yet generating these datasets is often the hardest and most time‑consuming step.\n\n\n\n \n \n \n \n \n Approach\n \n \n \n \n \n \n\n\nWe introduce ase‑uhal\, a Python tool developed through a PSDI Pilot Project (Oct 2025–Mar 2026).\nIt automates and accelerates dataset generation\, steering atomistic simulations toward the most informative configurations and avoiding redundant calculations.\nThe tool is available via pip install ase-uhal and integrates seamlessly with the ASE ecosystem.\n\n\n\n \n \n \n \n \n Innovation\n \n \n \n \n \n \n\n\nA “universal” extension of the Hyperactive Learning (HAL) framework makes the method compatible with modern foundation models that can be fine‑tuned.\nA new batched workflow significantly increases throughput compared to existing methods.\nDemonstrated on an InGaP alloy system\, where models trained on diverse data outperform those trained on random sampling.\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 James Kermode is a Professor in the School of Engineering at the University of Warwick (UoW)\, where he directs the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys CDT) and the Warwick Centre for Predictive Modelling (WCPM)\, both of which have strong synergies with PSDI activities across the full spectrum from theory and algorithm development through research software engineering to 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 MOFevaluator: A Cloud-Based Platform for Process-Informed Discovery of Metal–Organic Frameworks for Carbon Capture and Beyond\n \n \n \n \n \n Challenge\n \n \n \n \n \n \n\n\nMetal‑Organic Frameworks (MOFs) are promising for carbon capture and gas‑separation applications\, but moving from research to industrial‑scale decarbonization requires demonstrating economically viable production and deployment routes.\nIdentifying optimal MOFs requires understanding the full energy‑system context\, including CO₂ sources\, sinks\, costs\, and process constraints.\n\n\n\n \n \n \n \n \n Approach\n \n \n \n \n \n \n\n\nThe MOFevaluator project builds on the PrISMa platform\, which evaluates MOF performance based on:\n\nspecific CO₂ sources (power plants\, industry\, direct air capture)\npossible CO₂ sinks (geological storage\, mineralisation\, conversion\, etc.)\nregional constraints\n\n\nThis includes process modelling\, techno‑economic analysis\, and life‑cycle assessment\, ensuring that system‑scale requirements guide material discovery.\n\n\n\n \n \n \n \n \n Innovation\n \n \n \n \n \n \n\n\nMOFevaluator transforms the workflow from a local simulation tool into a cloud‑based platform with a fully searchable MOF materials database.\nResearchers can:\n\nvisualise data through an interactive web interface\nintegrate the database via API\nuse a streamlined environment to explore new opportunities for MOF discovery and application\n\n\nThe platform enables faster\, more scalable\, and system‑informed 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 Peter McCallum is a Research Software Engineer at Heriot-Watt University\, specialising in the architectures and development of web-based research systems. Having spent a decade in industry working on low-carbon energy system as a mechanical engineer\, he has since led software development activities in academic settings\, across themes including fluid dynamics\, control engineering\, energy networks\, built-environment modelling\, and for the new MOFevaluator web-platform. His main ambition is to build tools that not only support research but also translate quickly to applied industrial settings\, through distributed computing\, web-based visuals\, and API connected data via the cloud. \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 Susana Garcia Trained as a Chemical Engineer\, Susana Garcia is a Full Professor in Chemical and Process Engineering and the Associate Director on CCUS at the Research Center for Carbon Solutions (RCCS) in Heriot-Watt University (Edinburgh). An internationally recognised expert on low carbon separation processes\, CCUS and DAC technologies\, leading AI-driven materials discovery for industrial decarbonisation projects. \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_kaav0loGQp24L4VDOEPuuA \n\n\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-1/ LOCATION:Online\, Virtual Event\, Online CATEGORIES:Webinar END:VEVENT BEGIN:VEVENT DTSTART;TZID=Atlantic/Azores:20260507T140000 DTEND;TZID=Atlantic/Azores:20260507T150000 DTSTAMP:20260422T210458 CREATED:20260416T102949Z LAST-MODIFIED:20260420T155157Z 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 ofelectrochemical 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:20260422T210458 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