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DTSTART;VALUE=DATE:20260413
DTEND;VALUE=DATE:20260418
DTSTAMP:20260617T093806
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:20260617T093806
CREATED:20260310T164302Z
LAST-MODIFIED:20260501T130001Z
UID:36874-1776952800-1776956400@www.psdi.ac.uk
SUMMARY:Webinar: BioSimDR - A Collection of Data Tools and Infrastructure for Biomolecular Simulation
DESCRIPTION:This webinar illustrates how BioSimDR transforms scattered biomolecular simulation data into interoperable\, provenance-rich resources for broader reuse. \nThe recording of this webinar is now available on YouTube \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				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-biosimdr/
LOCATION:Online\, Virtual Event\, Online
CATEGORIES:Webinar
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BEGIN:VEVENT
DTSTART;TZID=Atlantic/Azores:20260430T150000
DTEND;TZID=Atlantic/Azores:20260430T160000
DTSTAMP:20260617T093806
CREATED:20260401T100337Z
LAST-MODIFIED:20260522T100616Z
UID:36980-1777561200-1777564800@www.psdi.ac.uk
SUMMARY:Webinar: From Project to Platform: New Resources on PSDI - Session 1
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				\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				Watch the recording\nYou can watch the recording of this webinar via our YouTube channel. Slides are available on Zenodo: https://zenodo.org/records/20273802 and https://zenodo.org/records/20273806 \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
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