BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Physical Sciences Data Infrastructure - ECPv6.17.3//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:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20221030T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20231029T010000 END:STANDARD 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:VEVENT DTSTART;VALUE=DATE:20260413 DTEND;VALUE=DATE:20260418 DTSTAMP:20251024T100532Z 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/London:20250331T120000 DTEND;TZID=Europe/London:20250404T140000 DTSTAMP:20250124T162158Z CREATED:20241030T172807Z LAST-MODIFIED:20250124T162158Z UID:35318-1743422400-1743775200@www.psdi.ac.uk SUMMARY:Chemical and materials machine learning school 2025 DESCRIPTION:Date of Event: 31st March 2025 12:00 – 4th April 2025 14:00\nLocation: Daresbury Laboratory\, in person event\nFee: £350 (covers 4 nights accommodation and catering) – if we secure additional sponsorship this fee will be reduced\nPre-requisites: Students will be expected to bring their own laptop\, to have a decent level of coding experience (see pre-requisites below) and provide a letter of support from their supervisor\n\nAll places for the school have now been finalised\, we look forward to hosting you soon. Thank you to everyone who applied. \n \n \n \n \n \n Description\nThis machine learning for materials training course is being run by the Physical Sciences Data Infrastructure (PSDI) initiative in collaboration with AIchemy\, with support from STFC-SCD\, PSDS\, CCP5 and CCP9 as a follow up to the very popular 2023 Machine learning for Atomistic Modelling Autumn School. This training is targeted towards PhD students\, in particular those in the Materials and Molecular Simulations field\, who have experience of coding but are not highly experienced with machine learning. The aim of this training is to introduce attendees to the latest methods of machine learning for the atomistic simulation of materials. \nThis training will encompass a number of talks and practical sessions\, focusing on the basics of machine learning\, machine learning interatomic potentials and graph neural networks. There will also be the opportunity for attendees to present a poster on their work. \n \n \n \n \n \n Learning outcomes\n\nAwareness of the state-of-the-art methods for machine learning for atomistic and molecular simulations\nHands on experience of using machine learning for atomistic and molecular simulations\n\nOutline Agenda – Draft\n \n \n \n \n \n \n\n\nSessions\nMonday\nTuesday\nWednesday\nThursday\nFriday\n\n\nMorning \n \nDescriptors\nNNs\nMLIPs general\nGenerative models\n\n\nAfternoon\nIntro to ML\nUnsupervised ML\nGNNs\nMLIPs – molecules/ materials\n \n\n\nEvening\nResearch talk – Kim Jelfs\nPosters\nBBQ\nResearch talk – Nong Arthrith\n \n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Pre-requisites\nStudents attending this course must already have a foundational level of Python experience and hands on experience of using Python in their research. You will be expected to provide your own laptop for the training course\, although software installation will not be required. A letter of support will be required from your supervisor alongside your application\, this will be requested by email following your application. This letter of support is to show the backing of your supervisor to attend the training and must be completed for your application to be assessed.  \nTimelines & Fees\nThe application deadline is 1st December 2024. Supervisors will be contacted for a letter of support following your application. All letters of support must be submitted by 6th December. You will be informed of the outcome of your application on 13th January 2025\, you will have to accept your place within 1 week and payment is required by 16th February 2025.  \nFood and 4 nights accommodation (Travelodge Warrington) is included in the £350 fee paid for this event\, travel to Daresbury (and public transport to /from the lab) is not included and will need to be covered by the attendee. If we are able to secure additional sponsorship for this event we will reduce the fee. Please note: places on this course are limited and in the event of oversubscription to the training course we will favour a diverse group of attendees.   \n \n \n \n \n \n All places for the school have now been finalised\, for those attending please ensure your payment is made promptly \n \n \n \n \n \n Organising Committee \n\nAlin-Marin Elena\, Scientific Computing Department STFC \nKeith Butler\, University College London\nReinhard Maurer\, University of Warwick \nKim Jelfs\, Imperial College London \nAlex Ganose\, Imperial College London\nIoan-Bogdan Magdău\, Newcastle University\nChris Mellor\, Imperial College London\nNicola Knight\, University of Southampton  URL:https://www.psdi.ac.uk/event/cammls-2025/ LOCATION:Daresbury Laboratory\, Keckwick Lane\, Daresbury\, WA4 4AD\, United Kingdom CATEGORIES:Training END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20230918T120000 DTEND;TZID=Europe/London:20230920T140000 DTSTAMP:20230919T080703Z CREATED:20230420T145709Z LAST-MODIFIED:20230919T080703Z UID:34451-1695038400-1695218400@www.psdi.ac.uk SUMMARY:Machine Learning for Atomistic Modelling Autumn School 2023 DESCRIPTION:All places for the school have now been finalised\, for those attending please ensure your payment is made by 1st September. \n \n \n \n \n \n \nDate of Event: 18th 12:00 – 20th September 14:00\nLocation: Daresbury Laboratory\, in person event\nFee: £100 (covers 2 nights accommodation and catering)\nPre-requisites: Students will be expected to bring their own laptop\, to have a decent level of coding experience (see pre-requisites below) and provide a letter of support from their supervisor\n\n \n \n \n \n \n Description\nThis machine learning for materials training course is being run by the Physical Sciences Data Infrastructure (PSDI) initiative in collaboration with PSDS\, AI4SD\, STFC-SCD and CCP5.This training is targeted towards PhD students\, in particular those in the Materials and Molecular Simulations field. The aim of this training is to introduce attendees to the latest methods of machine learning applied to atomistic simulation of materials. \nThis training will encompass a number of talks and practical sessions\, focusing on the basics of machine learning\, machine learning interatomic potentials and graph neural networks. There will also be the opportunity for attendees to present a poster on their work. \n \n \n \n \n \n Learning outcomes\n\nAwareness of the state-of-the-art methods for machine learning for atomic and molecular simulations\nHands on experience of using machine learning for atomic and molecular simulations\n\nOutline Agenda – Draft\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Day 1 – 18th\n\n12:00 – 13:00: Registration & Lunch\n13:00 – 13:30: Introduction\n13:30 – 15:00: Lecture Session: Basic introduction to ML topics – Reinhard Maurer\n15:00 – 15:30: Coffee Break\n15:30 – 17:15: Practical session: Basic ML worked example – Reinhard Maurer\n17:30 – 18:30: Research Seminar – Aron Walsh\n19:00: BBQ\n\n \n \n \n \n \n \n \n \n \n \n \n Day 2 – 19th\n\n09:00 – 10:30: Lectures (1h30) Machine Learning Interatomic Potentials – Ioan Magdau\n10:30 – 11:00: Coffee\n11:00 – 12:30: Practical Session MLIP (1h30) – Ioan Magdau + Alin-Marin Elena\n12:30 – 14:00: Lunch\n14:00 – 15:30: Practical Session MLIP (1h30) – Ioan Magdau + Alin-Marin Elena\n15:30 – 16:00: Coffee\n16:00 – 18:00: Lectures: GNN talks – Keith Butler + Alex Ganose\nPoster session evening + buffet / pizza\n\n \n \n \n \n \n \n \n \n \n \n \n Day 3 – 20th\n\n09:00 – 10:30: Practical Session: Building and training GNN – Keith Butler & Alex Ganose\n10:30 – 11:00: Coffee\n11:00 – 12:30: Practical Session: Using pre trained networks – Keith Butler & Alex Ganose\n12:30 – 14:00: Lunch & Departure\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Pre-requisites\nStudents attending this course must already have a foundational level of Python experience and hands on experience of using Python in their research. You will be expected to provide your own laptop for the training course\, although software installation will not be required. A letter of support will be required from your supervisor alongside your application. This letter of support is to show the backing of your supervisor to attend the training and must be completed on headed paper\, but does not need to be detailed. A template of the minimum required content is available in this word document. \nTimelines\nThe application deadline is 30th June 2023 (including letter of support from your supervisor). Applications are now closed. You will be informed of the outcome of your application on 1st August\, you will have to accept your place within 1 week and payment is required by 1st September. These timelines have been amended due to the exceptionally high number of applications we received.  \nFood and 2 nights accommodation is included in the £100 fee paid for this event\, travel to Daresbury is not included and will need to be covered by the attendee. Please note: places on this course are limited and in the event of oversubscription to the training course we will favour a diverse group of attendees.   \n \n \n \n \n \n All places for the school have now been finalised\, for those attending please ensure your payment is made by 1st September. \n \n \n \n \n \n Organising Committee \n\nAlin-Marin Elena\, Scientific Computing Department STFC \nKeith Butler\, Queen Mary University London \nReinhard Maurer\, University of Warwick \nKim Jelfs\, Imperial College London \nAlex Ganose\, Imperial College London\nSimon Coles\, University of Southampton \nSamantha Kanza\, University of Southampton \nNicola Knight\, University of Southampton  URL:https://www.psdi.ac.uk/event/machine-learning-autumn-school-2023/ LOCATION:Daresbury Laboratory\, Keckwick Lane\, Daresbury\, WA4 4AD\, United Kingdom CATEGORIES:Training ATTACH;FMTTYPE=image/png:https://www.psdi.ac.uk/wp-content/uploads/2023/04/MLAutumnSchool-flyer.png END:VEVENT END:VCALENDAR