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DTSTART;TZID=UTC:20260716T090000
DTEND;TZID=UTC:20260716T170000
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CREATED:20260505T135703Z
LAST-MODIFIED:20260702T142909Z
UID:9330-1784192400-1784221200@aichemy.ac.uk
SUMMARY:Transitioning from FAIR to AI Ready Data in the Physical Sciences
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE16 JULY 2026 TIME10:00 – 16:00 COSTFREE TALK SUBMISSION DEADLINE12 JUNE 2026 REGISTRATION DEADLINE30 JUNE 2026 \n\n\n\nbook now \n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF SOUTHAMPTONHighfield Campus\, Southampton\, SO17 1BJ \n\n\n\n\n\n\n\n\nIn recent years\, the physical sciences community has been generating increasingly large and complex datasets\, at a scale that is now beyond what can be fully explored or analysed by humans alone. As a result\, researchers are turning to AI and machine‑learning techniques\, which have matured significantly and offer powerful new ways to extract insight from data. However\, while the adoption of FAIR data principles has improved data sharing and reuse\, experience is showing that FAIR does not necessarily mean AI‑ready. Many datasets remain difficult to use effectively in AI and Machine Learning models.This interactive workshop has been co-created by the Physical Sciences Data Infrastructure (PSDI) and the AI in Chemistry Hub (AIchemy). It aims to bring together researchers\, data professional and infrastructure developers to facilitate knowledge exchange and explore what it truly means to be “AI Ready”. The workshop is comprised of invited presentations\, lightning talks from participants and interactive discussion sessions. The talks will share current practices\, highlighting successes and challenges\, and the discussion sessions will explore the practical approaches and tools for evaluating and improving AI readiness. \n\n\n\n\n\n\n\nWho Should Attend: \n\n\n\nThis in-person event is aimed at anyone interested in dataset standards\, curation\, and developing robust methods to assess the applicability and reliability of data for reuse. It will be particularly relevant for researchers and research software engineers working with data and AI/ML\, data stewards and research data managers\, infrastructure and platform developers\, and scientists interested in enabling future reuse of their datasets. \n\n\n\n\n\nAgenda \n\n\n\n10:00-10:30Registration & Refreshments10:30-10:35Housekeeping & Welcome10:35-10:45Introduction to PSDI10:45-10:55Introduction to AIchemy10:55-11:10Setting the Scene: From FAIR to AI-Ready11:10-11:25Coffee Break & Networking11:25-12:45Invited Speakers (Matthew Partridge\, Aileen Day\, Nessa Carson\, Otello Roscioni)12:45-13:30Networking Lunch13:30-14:00Participant Lightning Talks14:00-14:15Introduction to Discussion Sessions14:15-14:45Discussion Sessions Part 114:45-15:00Coffee Break & Networking15:00-15:30Discussion Sessions Part 215:30-16:00Feedback & Wrap Up\n\n\n\n\n\nCall for Lightning Talks \n\n\n\nWe invite submissions for short lightning talks exploring the challenges\, opportunities\, and practical experiences involved in creating AI-ready datasets within the physical sciences. This is an opportunity to share emerging ideas\, real-world case studies\, and lessons learned from working with data intended for AI and machine-learning applications.  Topics may include\, but are not limited to: \n\n\n\n\nExperiences in developing or curating AI-ready datasets\n\n\n\nChallenges in preparing FAIR data for AI and machine learning use\n\n\n\nData quality\, metadata\, interoperability\, and standardisation\n\n\n\nBenchmarking\, validation\, and reproducibilityInfrastructure\, tooling\, and workflow development\n\n\n\nCommunity needs\, open challenges\, and proposed solutions\n\n\n\n\nDuring registration\, participants will be able to indicate their interest in presenting a lightning talk. Talk Submission Deadline: 12th June The organising team will review submissions and notify successful applicants by 26th June 2026. \n\n\n\n\n\nInvited Speakers\n\n\n\n\n\n\n\n\n\nDr Matthew PartridgeUniversity of Southampton\n\n\n\n\n\nDr Aileen DayUniversity of Southampton\n\n\n\n\n\nDr Nessa CarsonAstraZeneca\n\n\n\n\n\nDr Otello RoscioniUniversity of Southampton\n\n\n\n\n\n\n\n\n\n\n\nContact Details\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk \n\n\n\n\n\nThe Organising Committee\n\n\n\nAIchemy HubPSDIDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Samantha Pearman-KanzaNicola KnightVictoria Hooper
URL:https://aichemy.ac.uk/event/transitioning-from-fair-to-ai-ready-data-in-the-physical-sciences/
CATEGORIES:Symposium
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260721T090000
DTEND;TZID=UTC:20260721T170000
DTSTAMP:20260818T155806Z
CREATED:20260206T072607Z
LAST-MODIFIED:20260818T155806Z
UID:7121-1784624400-1784653200@aichemy.ac.uk
SUMMARY:Gen AI In Chemistry Education
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE21 JULY 2026 TIME09:00 – 17:00 COSTFREE \n\n\n\nREGISTRATION CLOSED\n\n\n\nRead highlights\n\n\n\nView Agenda\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF LIVERPOOLCentral Teaching Labs \n\n\n\n\n\n\n\n\nThis UK national symposium brings together chemistry educators\, students\, and policy leads to accelerate the integration of Generative AI (GenAI) methods in chemistry education. Building on last year’s event\, the focus moves decisively beyond abstract debate toward classroom-ready practice\, shared resources\, and cross-institutional learning.Aligned with the AIchemy Hub mission\, the workshop will advance the UK-wide conversation through three interconnected themes that reflect real teaching\, learning\, and institutional needs: \n\n\n\nPractical implementation of GenAI in chemistry teaching and laboratoriesExplore how AI can be meaningfully embedded into undergraduate teaching and laboratory practice. Sessions will focus on the co-design of AI-integrated experiments\, inclusive laboratory tools\, and transferable teaching resources. Participants will contribute to shaping a shared repository of AI-enabled laboratory and assessment activities. \n\n\n\nAI in assessment\, feedback\, and curriculum designExamine how AI can act as a co-pilot for educators\, supporting assessment design\, feedback generation\, curriculum review\, and bias reduction. This theme supports the development of shared tools and best practice to help bridge the AI–chemistry skills gap for both staff and students. \n\n\n\nInstitutional strategy\, policy\, and ethics grounded in real practiceEngage with institution-level strategies and ethical frameworks for the responsible use of GenAI in chemistry education. By comparing approaches across UK universities\, this strand will highlight shared principles\, practical challenges\, and transferable exemplars to support ethical implementation in teaching and assessment. \n\n\n\n\n\n\n\nCall for Lightning Talk Abstracts \n\n\n\nWe invite submissions for short lightning talks that showcase practical and innovative uses of AI in chemistry education. This is an opportunity to share your practice\, ideas\, and lessons learned with colleagues from across the UK\, and to contribute to a growing national conversation on how AI is shaping chemistry teaching and learning. We particularly welcome abstracts featuring case studies from teaching practice\, AI-enabled laboratory experiments\, assessment and feedback approaches\, curriculum design initiatives\, and student–staff co-created projects.During registration\, you will be able to indicate your interest in giving a lightning talk and submit a short abstract\, after which the organising team will be in touch with further details.  \n\n\n\n\n\n\n\nWho Should Attend: \n\n\n\nThis workshop is aimed at academics working in chemistry with an interest in teaching and education\, as well as teachers and staff from colleges and secondary schools who want to better understand future university pathways for their students. It will also be valuable for anyone seeking insight into how artificial intelligence is shaping the next generation of chemistry education across the UK. \n\n\n\nWhat you’ll gain: \n\n\n\n\nGain insight into the current UK higher-education curriculum landscape for AI in chemistry\n\n\n\nUnderstand how GenAI is being implemented across undergraduate and postgraduate taught courses\, in both lectures and laboratories\n\n\n\nExplore challenges\, risks\, and opportunities in real-world adoption\n\n\n\nDevelop ideas for new lab-based experiments and training using AI methods such as machine learning (ML)\, convolutional neural networks (CNNs)\, and Bayesian optimisation (BO)\n\n\n\n\n\n\nSpeakers\n\n\n\n\n\nDr. Ghada RabahNC State University\n\n\n\n\n\nDr. Jason SonnenbergOhio State University\n\n\n\n\n\nDr. Peter AlstonBPP University\n\n\n\n\n\nProf. Kathryn CowtonUniversity of York\n\n\n\n\n\n\n\nDr. Rebecca JonesImperial College London\n\n\n\n\n\nDr. Benji Fenech-Salerno\,Imperial College London\n\n\n\n\n\nDr. Denise HoughUniversity of Glasgow\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nContact Details\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk \n\n\n\n\n\nThe Organising Committee\n\n\n\nAIchemy HubUniversity of WarwickUniversity of GlasgowDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr. Chris Mellor (Imperial)Aysel Sarzosa Llerena (Imperial)Tom RitchieDr Dani PearsonDr Ciorsdaidh Watts
URL:https://aichemy.ac.uk/event/gen-ai-in-chemistry-education/
CATEGORIES:Symposium
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260901T090000
DTEND;TZID=UTC:20260903T170000
DTSTAMP:20260908T131935Z
CREATED:20260313T125151Z
LAST-MODIFIED:20260908T131935Z
UID:8245-1788253200-1788454800@aichemy.ac.uk
SUMMARY:Chemical Artificial Intelligence in Homogeneous Catalysis (ChemAICat) Workshop
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE1 -3 SEPTEMBER 2026 TIME09:00 – 17:00 COST£75 – ACADEMIA £150 – INDUSTRY REGISTRATION DEADLINE3 JULY 2026 \n\n\n\nApplications closed\n\n\n\nagenda\n\n\n\nVIEW HIGHLIGHTS\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF LIVERPOOLRendall Building\, Seminar Room 4\, Liverpool\, L69 7WW \n\n\n\n\n\n\n\n\n\n\n\n\nAbout the Workshop\n\n\n\nThe Chemical Artificial Intelligence in Homogeneous Catalysis (ChemAICat) workshop held at University of Liverpool offers a practical guide to different AI techniques that can be applied to accelerate or complement research in homogeneous catalysis. It is especially recommended for experimental and computational chemists with basic or no Python programming experience. The classes aim to provide these researchers with ready-to-use workflows that they can introduce into their routine research tasks. Some of the protocols covered include: \n\n\n\n\nBasics of Python and cheminformatics\n\n\n\nAutomated descriptor generation from ChemDraw and CSV files\n\n\n\nData-driven and efficient chemical exploration for catalyst sampling\n\n\n\nAI-driven optimization of reaction conditions\n\n\n\nAI-driven catalyst discovery\n\n\n\n\n\n\n\n\nWho Should Attend: \n\n\n\nThis workshop is aimed at experimental and computational chemists working in homogeneous catalysis with little or no experience in Python. The workshop is suitable for the following career stages: PhD students\, postdoctoral researchers and Early Career Academics. \n\n\n\n\n\n\n\nPre-Requisites: \n\n\n\nTo help you get the most from the workshop\, please ensure the following are completed in advance: \n\n\n\n\nAttendees must bring their own laptops to run the programs used during the workshop. Windows\, macOS and Linux operating systems are supported.\n\n\n\nComplete a preliminary setup to install the required Python environments. Instructions will be provided a few weeks before the workshop and should take 30-60 minutes to complete.\n\n\n\n(Optional) Share custom problems in advance that could be included in the problem-solving sessions. This will allow the workshop to focus on topics that are most relevant to the audience\n\n\n\n\n\n\nHow to apply\n\n\n\nPlaces are limited and to ensure a balanced mix of expertise and perspectives we are asking applicants to apply. As demand is expected to be high\, we ask all interested participants to complete the application form by 3rd July 2026 and decisions will be given to applicants by 10th July 2026.Please note: all bookings are non-refundable. \n\n\n\n\n\n\n\n\n\n\n\n\n\nAccommodation and Travel\n\n\n\nPlease note that the registration fee does not include accommodation\, travel or subsistence. Participants are responsible for arranging their own accommodation and transport during the ChemAICat Workshop.We are happy to recommend the Novotel Liverpool Paddington Village\, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms\, breakfast options and easy access to local amenities.A social networking event will be hosted on one evening (day to be confirmed) during the school and is included in the registration.For those seeking alternative options\, Liverpool offers a wide range of hotels\, serviced apartments\, and budget accommodations within easy reach of the University.Liverpool is well-connected by rail\, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car\, parking is available at the Paddington Village Car Park\, located close to the University and the recommended hotel. \n\n\n\nAirports\n\n\n\nLiverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.Manchester Airport (MAN) – Around 1 hour by train or car\, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options. \n\n\n\n\n\n\n\n\n\n\n\n\n\nCourse Supervisors\n\n\n\nDr. Jamie Cadge (University of Bath) \n\n\n\nDr. Ruben Laplaza (IIQ\, University of Seville-CSIC) \n\n\n\nDr. Thijs Stuyver (PSL University) \n\n\n\nDr. Juan V. Alegre-Requena (ISQCH\, University of Zaragoza-CSIC) \n\n\n\n\n\n\n\n\n\n\n\n\n\nContact Details\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk \n\n\n\n\n\nThe Organising Committee\n\n\n\nAIchemy HubUniversity of LiverpoolCourse SupervisorsDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Jean-Francois AymeDr. Jamie Cadge (University of Bath)Dr. Ruben Laplaza (IIQ\, University of Seville-CSIC)Dr. Thijs Stuyver (PSL University)Dr. Juan V. Alegre-Requena (ISQCH\, University of Zaragoza-CSIC)
URL:https://aichemy.ac.uk/event/chemical-artificial-intelligence-in-homogeneous-catalysis-chemaicat/
CATEGORIES:Symposium
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260908T000000
DTEND;TZID=UTC:20260908T235959
DTSTAMP:20260917T064105Z
CREATED:20260708T095843Z
LAST-MODIFIED:20260917T064105Z
UID:10823-1788825600-1788911999@aichemy.ac.uk
SUMMARY:Transferable Opportunities for AI \nin Materials Chemistry
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE8 SEPTEMBER 2026 COSTFREE \n\n\n\nsold out – join the waiting list\n\n\n\nview agenda\n\n\n\nread highlights\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nThe Spine Building2 Paddington Village\, Liverpool\, L69 7ZD \n\n\n\n\n\n\n\n\nAIchemy and the EPSRC Programme Digital Navigation for Chemical Space are delighted to host a one-day symposium\, Transferable Opportunities for AI in Materials Chemistry\, taking place on 8th September 2026 at The Spine\, Liverpool.Artificial intelligence is advancing at an unprecedented pace\, with many of the most exciting developments emerging from computer science. While these innovations are transforming multiple sectors\, there is enormous potential to accelerate the translation of these advances into chemistry and materials discovery. This symposium will bring together experts from computer science to showcase the latest advances in artificial intelligence and foster new interdisciplinary connections with the chemical sciences. By highlighting emerging AI capabilities from across computer science\, the programme will encourage attendees to think beyond current applications\, explore how these methods could be adapted to chemical and materials research\, and spark collaborations that drive future scientific innovation. \n\n\n\nWhat you’ll gain\n\n\n\n\nDiscover the latest advances in artificial intelligence emerging from computer science\n\n\n\nExplore how transferable AI methods could accelerate chemical and materials discovery\n\n\n\nInsights into new computational techniques and emerging AI capabilities\n\n\n\nEngage in interdisciplinary discussions with researchers from computer science\, chemistry and materials science\n\n\n\nBuild new collaborations that could shape the next generation of AI-enabled scientific research\n\n\n\n\nWho should attend?\n\n\n\n\nComputer scientists developing AI and machine learning methods\n\n\n\nResearchers in chemistry\, materials science and chemical engineering\n\n\n\nEarly Career Researchers and PhD students \n\n\n\nIndustry scientists interested in AI-enabled materials discovery \n\n\n\nAnyone interested in interdisciplinary AI research across chemistry and materials science\n\n\n\n\n\n\nSpeakers \n\n\n\n\n\nDr Vladimir Gusev University of Liverpool\n\n\n\n\n\nProfessor Francesca Toni Imperial\n\n\n\n\n\nProfessor Michael Wooldridge University of Oxford\n\n\n\n\n\nDr Louise Dennis University of Manchester\n\n\n\n\n\n\n\nProfessor Frans OliehoekTu Delft\n\n\n\n\n\n Professor Danushka BollegalaUniversity of Liverpool\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAccommodation and Travel\n\n\n\nWe are happy to recommend the Novotel Liverpool Paddington Village\, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms\, breakfast options and easy access to local amenities. \n\n\n\nLiverpool is well-connected by rail\, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car\, parking is available at the Paddington Village Car Park\, located close to the University and the recommended hotel. \n\n\n\nAirports:\n\n\n\n\nLiverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.\n\n\n\nManchester Airport (MAN) – Around 1 hour by train or car\, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options.\n\n\n\n\n\n\n\n\nContact details:\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk. \n\n\n\n\n\nThe Organising Committee:\n\n\n\nAlchemy HubUniversity of LiverpoolDr. Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Vikki Berryman
URL:https://aichemy.ac.uk/event/transferable-opportunities-for-ai-in-materials-chemistry/
CATEGORIES:Symposium
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260914T000000
DTEND;TZID=UTC:20260916T235959
DTSTAMP:20260917T091602Z
CREATED:20260512T131430Z
LAST-MODIFIED:20260917T091602Z
UID:9541-1789344000-1789603199@aichemy.ac.uk
SUMMARY:Data Intensive Science School
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE14 – 16 SEPTEMBER 26 ACADEMIC PARTICIPANTS£75 INDUSTRY PARTICIPANTS£150 \n\n\n\nApplications closed\n\n\n\n View agenda \n\n\n\nread highlights\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUniversity of Liverpool CampusRendall Building\, Liverpool\, L69 7WW \n\n\n\n\n\n\n\n\n\n\n\n\nJoin AIchemy Hub and LIV.INNO for an intensive hands-on Data Science School designed to equip researchers\, students\, and innovators with practical skills at the intersection of AI\, scientific computing\, and open-source research.This interdisciplinary programme brings together experts from academia and industry to deliver workshops and talks covering modern data science workflows\, scalable computing\, machine learning\, agentic AI systems\, scientific software development\, and research reproducibility.Participants will gain practical experience using cutting-edge tools and frameworks while exploring how AI and data-driven approaches are transforming scientific discovery.The school combines hands-on workshops\, interactive tutorials\, and expert-led talks focused on real-world scientific and data-intensive applications. \n\n\n\n\n\nWho should apply\n\n\n\nThis school is designed for: \n\n\n\n\nPhD students and Early Career Researchers\n\n\n\nData scientists and computational researchers\n\n\n\nScientists interested in AI and machine learning\n\n\n\nResearchers working with large or complex datasets\n\n\n\nAnyone interested in modern open-source scientific computing\n\n\n\n\nWhat you’ll gain\n\n\n\nParticipants will: \n\n\n\n\nGain practical experience with modern data science tools \n\n\n\nLearn directly from experts in AI and computational science \n\n\n\nBuild skills in scalable computing and scientific software development \n\n\n\nExplore emerging trends in agentic AI and open-source science \n\n\n\nNetwork with researchers across disciplines\n\n\n\n\n\n\nProvisional Programme \n\n\n\n\nApache Spark Workshop (4 hours) – Learn scalable data processing and distributed computing techniques for handling large scientific datasets.\n\n\n\nMachine Learning: Data Collection & Preparatio (4 hours) – Explore the foundations of building robust ML pipelines\, from data acquisition to preprocessing and feature engineering.\n\n\n\nAgentic AI for Data Analysis (1 hour talk) Discover how autonomous AI agents are reshaping data analysis\, scientific workflows\, and research productivity.\n\n\n\nGit Workshop (2 hours) Develop essential version control skills for collaborative coding\, reproducible research\, and software development.\n\n\n\nOpen Source Science (1 hour talk) Examine the growing importance of open-source ecosystems in accelerating scientific innovation and collaboration.\n\n\n\nPyAutoFit Workshop (2 hours) Introduction to probabilistic modelling and automated model fitting using PyAutoFit for scientific applications.\n\n\n\nPublishing Code (2 hours) Learn best practices for sharing\, documenting\, packaging\, and publishing scientific software and research code.\n\n\n\nReal World Challenges Session (2 hours) – Apply your skills to practical scientific and data-driven problems inspired by current research challenges.\n\n\n\nHow Not to Make NumPy Slow (2 hours) Improve performance and efficiency in scientific Python workflows through optimisation strategies and vectorised computation.\n\n\n\n\n\n\nSpeakers & Trainers \n\n\n\n\n\nDr Edward BennettSwansea University\n\n\n\n\n\nDr Prakriti KayasthaUCL\n\n\n\n\n\nDr Bradley MartinUCL\n\n\n\n\n\nDr James NightingaleNewcastle University\n\n\n\n\n\n\n\nDr Louise ButcherSTFC/Hartree\n\n\n\n\n\nDr Abdoulatif CisseUniversity of Liverpool\n\n\n\n\n\nDr Alex HillUniversity of Liverpool\n\n\n\n\n\nProfessor Reecha SofatUniversity of Liverpool\n\n\n\n\n\n\n\n\n\n\n\nAccommodation and Travel\n\n\n\nPlease note that the registration fee does not include accommodation\, travel or subsistence. Participants are responsible for arranging their own accommodation and transport during the Data Science School. \n\n\n\nWe are happy to recommend the Novotel Liverpool Paddington Village\, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms\, breakfast options and easy access to local amenities. \n\n\n\nA social networking event will be hosted on one evening during the school and is included in the registration. \n\n\n\nFor those seeking alternative options\, Liverpool offers a wide range of hotels\, serviced apartments\, and budget accommodations within easy reach of the University. \n\n\n\nLiverpool is well-connected by rail\, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car\, parking is available at the Paddington Village Car Park\, located close to the University and the recommended hotel. \n\n\n\nAirports:\n\n\n\n\nLiverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.\n\n\n\nManchester Airport (MAN) – Around 1 hour by train or car\, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options.\n\n\n\n\n\n\n\n\nHow to Apply:\n\n\n\nPlaces are limited and to ensure a balanced mix of expertise and perspectives we are asking applicants to apply. As demand is expected to be high\, we ask all interested participants to complete the application form by 10th July 2026 and decisions will be given to applicants by 17th July 2026. \n\n\n\nPlease note: all bookings are non-refundable. \n\n\n\n\n\n\n\nContact details:\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk. \n\n\n\n\n\n\n\n\nThe Organising Committee:\n\n\n\nAlchemy HubLIV.INNODr. Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr. Alex HillNaomi Smith
URL:https://aichemy.ac.uk/event/data-intensive-science-school/
CATEGORIES:Training School
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260923T090000
DTEND;TZID=UTC:20260923T170000
DTSTAMP:20260924T080729Z
CREATED:20260502T120152Z
LAST-MODIFIED:20260924T080729Z
UID:8963-1790154000-1790182800@aichemy.ac.uk
SUMMARY:Demystifying Agentic AI & AI Agents Workshop
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE23 SEPTEMBER 2026 TIME09:00 – 17:00 COST£25 REGISTRATION DEADLINE11 SEPTEMBER 2026 \n\n\n\nbook now\n\n\n\nView Agenda\n\n\n\nRead highlights\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF LEEDSCharles Thackrah Building\, 90 Clarendon Rd\, Woodhouse\, Leeds LS2 9LB \n\n\n\n\n\n\n\n\n\n\n\n\nJoin us for this one day workshop and unlock the potential of AI agents and retrieval-augmented generation (RAG) in chemical science. This interactive\, hands-on workshop is designed to bring together researchers from academia and industry to explore how emerging AI approaches can transform chemical research and innovation. Whether you are new to these technologies or already experimenting with them\, this event will provide both practical skills and a collaborative space to shape future applications. \n\n\n\n\n\n\n\nWhy attend? \n\n\n\nParticipants will gain: \n\n\n\n\nHands-on experience with RAG models and agentic AI systems\, guided by experts\n\n\n\nA practical understanding of real-world challenges when applying these tools to chemistry\n\n\n\nOpportunities to collaborate across disciplines\, connecting early-career researchers\, academics\, and industry professionals\n\n\n\nThe chance to co-develop ideas and proposals with clear industrial relevance\n\n\n\n\n\n\n\n\nWho Should Attend: \n\n\n\nThis workshop is open to researchers across academia and industry who are interested in applying AI to chemical science.We particularly encourage: \n\n\n\n\nPhD students\n\n\n\nPostdoctoral researchers\n\n\n\nEarly Career Independent Academic\n\n\n\nPrincipal Investigators and industry professionals to join strategic discussions shaping future challenges and collaborations\n\n\n\n\nWhat you’ll gain: \n\n\n\nThe day combines practical training with collaborative innovation: \n\n\n\n\nA hands-on RAG workshop\, delivered by Data Revival\n\n\n\nA hackathon session focused on building AI agents for chemistry\n\n\n\nA sandpit session for PIs and industry to define challenges\, methodologies\, and collaborative opportunities\n\n\n\nStructured discussions to develop clear\, proposal-ready ideas with defined outcomes and teams\n\n\n\n\n\n\nContact Details\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk \n\n\n\n\n\nThe Organising Committee\n\n\n\nAIchemy HubUniversity of LeedsUniversity of HullDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Professor Bao NguyenDr Sam MundayDr Koorosh Aslansefat
URL:https://aichemy.ac.uk/event/demystifying-agentic-ai-ai-agents-workshop/
CATEGORIES:Symposium
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DTSTART;TZID=UTC:20260923T140000
DTEND;TZID=UTC:20260923T150000
DTSTAMP:20260925T101934Z
CREATED:20260911T102615Z
LAST-MODIFIED:20260925T101934Z
UID:13736-1790172000-1790175600@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – September 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE23rd September 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nFrom In Silico Design to Automated Synthesis: An AI-Driven Framework for Late-Stage Functionalisation Autonomous chemical research with robots and agents \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nDr. Linjiang Chen – University of Birmingham \n\n\n\nTalk Title: Autonomous chemical research with robots and agents \n\n\n\nI will introduce our robotic AI chemists\, integrating knowledge acquisition\, theoretical modelling\, machine learning\, and automated experimentation [1\,2]\, with catalyst discovery for plastic recycling illustrating the connection between data-driven optimisation and hypothesis-guided exploration [3]. \n\n\n\nI will then describe our robotic AI chemistry research infrastructure spanning more than 2\,600 m² with 605 registered workstations. Modular architecture and reusable skills connect heterogeneous instruments\, computational tools and decision models while preserving workflow traceability [4]. \n\n\n\nHowever\, infrastructure alone does not ensure autonomy. Across 4\,608 agent trials\, only 3.3% produced expert-assessed executable workflows; the best configuration achieved 28.1%. A five-round adaptation test revealed local parameter adjustments but no workflow-level replanning or analytical-method redesign [5]. \n\n\n\nTo address the operational side of this gap\, we developed a computable representation of the physical laboratory\, combining typed research objects\, capability-bound operations\, and a compositional workflow algebra [6]. Plans become programs over evolving sample and container states\, checked through structural analysis and stateful simulation before dispatch. Four workflow examples illustrate this verification\, which establishes consistency with encoded constraints—not chemical success. Together\, these studies highlight why autonomous chemistry requires a computable\, testable interface between scientific reasoning and physical action. \n\n\n\nReferences \n\n\n\n\nZhu\, Q. et al. Natl. Sci. Rev. 9\, nwac190 (2022). DOI: 10.1093/nsr/nwac190.\n\n\n\nSong\, T. et al. J. Am. Chem. Soc. 147\, 12534–12545 (2025). DOI: 10.1021/jacs.4c17738.\n\n\n\nYu\, Y. et al. J. Am. Chem. Soc. 148\, 4635–4644 (2026). DOI: 10.1021/jacs.5c20630.\n\n\n\nLi\, X.\, Chen\, L. et al. ACS Nano 20\, 24593–24603 (2026). DOI: 10.1021/acsnano.6c15286.\n\n\n\nGuo\, L. et al. Stress-testing large language model agents in a robotic chemistry laboratory. arXiv:2607.23045 (2026). Preprint.\n\n\n\nLi\, X. et al. A computable representation of the physical laboratory enables verifiable workflows. arXiv:2609.03621 (2026). Preprint.\n\n\n\n\nLyubomir Kotopanov – University of Liverpool \n\n\n\nTalk Title: From In Silico Design to Automated Synthesis: An AI-Driven Framework for Late-Stage Functionalisation \n\n\n\nSelf-driving laboratories guided by machine learning algorithms hold the potential to significantly accelerate discoveries in the chemical sciences. However\, to fully exploit the opportunities offered by autonomous laboratories\, medicinal chemistry faces a major bottleneck – not only in the physical preparation of molecules\, but also in computer-based retrosynthetic planning. Current algorithms favour well-established chemistries\, whereas newer\, potentially more efficient reactions are under-prioritised because they are less explored in the literature. We present a flexible data-driven framework to guide high-throughput late-stage functionalisation of drug compounds. This pipeline generates diverse therapeutic candidates in silico with promising properties and synthesisability via user-defined late-stage functionalisation reactions with predicted conditions. Our results suggest that data-driven methods can be utilised to establish a robust and standardised workflow for the autonomous generation of promising drug-like molecules that can be efficiently accessed via innovative reaction pathways. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nDr. Linjiang ChenAssistant Professor of Digital Chemistry\n\n\n\n\n\nLyubomir KootopanovPhD Student\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-september-2026/
CATEGORIES:Webinar
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DTSTART;TZID=UTC:20261021T140000
DTEND;TZID=UTC:20261021T150000
DTSTAMP:20260911T130521Z
CREATED:20260911T121034Z
LAST-MODIFIED:20260911T130521Z
UID:13777-1792591200-1792594800@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – October 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE21st October 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\nREGISTER HERE\n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nAssistant Prof. Dr. Esther Heid – TU Wien \n\n\n\nTalk Title: Flow Matching for Chemical Reaction Modeling \n\n\n\nMachine learning has become a powerful tool for predicting and designing molecular properties\, materials\, and chemical reactions. While traditional quantum chemical methods provide accurate transition states and reaction barriers\, they remain computationally prohibitive for high-throughput applications. Recent advances in generative modeling\, particularly flow matching\, offer a promising alternative\, enabling rapid prediction of three-dimensional molecular geometries or transition state structures from simple graph inputs. In this talk\, I will present our work on applying flow matching to multiple challenges in reaction modeling: generating transition state geometries\, predicting reaction barrier heights\, steering outputs to satisfy geometric constraints like chirality\, and extending these methods to excited-state chemistry. I will discuss how these approaches achieve orders-of-magnitude speedups while maintaining near-quantum-chemical accuracy\, and evaluate their practical impact when embedded in automated library generation pipelines with or without continuous learning strategies. \n\n\n\nECR Speaker: TBC \n\n\n\nTalk Title: TBC \n\n\n\nFollowing the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nAsst. Prof. Esther HeidAssistant Professor for Machine Learning for Sustainable Chemistry\n\n\n\n\n\nECR Speaker:TBC \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-october-2026/
CATEGORIES:Webinar
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DTSTART;TZID=UTC:20261118T140000
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DTSTAMP:20260911T125340Z
CREATED:20260911T124549Z
LAST-MODIFIED:20260911T125340Z
UID:13827-1795010400-1795014000@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – November 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE18th November 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\nREGISTER HERE\n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nAsst. Prof. Varinia Bernales – University of Toronto \n\n\n\nTalk Title: TBC \n\n\n\nECR Speaker: TBC \n\n\n\nTalk Title: TBC \n\n\n\nFollowing the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nAsst. Prof. Varinia Bernales Assistant Professor | Research Director\n\n\n\n\n\nECR Speaker TBC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-november-2026/
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DTSTART;TZID=UTC:20261123T000000
DTEND;TZID=UTC:20261127T235959
DTSTAMP:20260825T072613Z
CREATED:20260220T124659Z
LAST-MODIFIED:20260825T072613Z
UID:7897-1795392000-1795823999@aichemy.ac.uk
SUMMARY:Winter School: Robotics and AI for Materials Chemistry 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE23 – 27 NOVEMBER 26 ACADEMIC PARTICIPANTS£150 INDUSTRY PARTICIPANTS£300 \n\n\n\nAPPLications closed\n\n\n\n agenda COMING SOON\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nNovotel Hotel\, University of Liverpool Campus3 Paddington Village\, Liverpool\, L69 7ZD \n\n\n\n\n\n\n\n\n\n\n\n\nThe Robotics and AI for Materials Chemistry Winter School is a five-day intensive training programme focused on digital and automated chemistry\, robotic systems\, and AI-driven scientific discovery. Hosted at the University of Liverpool\, the school is designed for PhD students and early-career researchers\, or those new to digital chemistry and AI\, and who want to build their capabilities in this rapidly evolving field. A strong working knowledge of Python is required to fully benefit from the practical sessions and technical content. \n\n\n\nThis Robotics and AI for Materials Chemistry Winter School will: \n\n\n\n\nProvide essential skills in digital chemistry\n\n\n\nStrengthen understanding of AI and machine learning\, building on participants’ existing knowledge\n\n\n\nOffer hands-on experience with key tools and techniques for automated\, intelligent lab environments\n\n\n\n\nParticipants will explore a range of cutting-edge topics\, including: \n\n\n\n\nDigital twins for robotic chemists\n\n\n\nBayesian optimisation for chemical discovery\n\n\n\nAI-driven robotic chemists\n\n\n\nRobotic manipulation for lab automation\n\n\n\nSimulation of robotic chemists\n\n\n\nComputer vision-led chemistry\n\n\n\nMulti-modal machine learning for science\n\n\n\nAgentic AI-based discovery\n\n\n\nHuman-in-the-loop robotic discovery\n\n\n\n\nAfter a hugely successful 2025 Winter School\, we’re delighted to bring it back for 2026. Take a look at last year’s programme and highlights to see what to expect. \n\n\n\nHow to Apply:\n\n\n\nPlaces are limited and to ensure a balanced mix of expertise and perspectives we are asking applicants to apply. As demand is expected to be high\, we ask all interested participants to complete the application form by 24th August 2026 and decisions will be given to applicants by 31st August 2026. \n\n\n\nPlease note: all bookings are non-refundable. \n\n\n\n\n\nProvisional Programme – Coming Soon\n\n\n\n\n\nSpeakers – More to be announced\n\n\n\n\n\nProf. Michael MistryUniversity of Edinburgh\n\n\n\n\n\nDr. Lauren Ye SeolUniversity College London\n\n\n\n\n\nProf. Andi ZhangUniversity of Warwick\n\n\n\n\n\nDr. Chenghao LiuCaltech\n\n\n\n\n\n\n\nAlexandra GessnerAstraZeneca\n\n\n\n\n\nProf. Simos GerasimouUniversity of York\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAccommodation and Travel\n\n\n\nPlease note that the registration fee does not include accommodation\, travel or subsistence. Participants are responsible for arranging their own accommodation and transport during the Winter School. \n\n\n\nWe are happy to recommend the Novotel Liverpool Paddington Village\, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms\, breakfast options and easy access to local amenities. \n\n\n\nA social networking event will be hosted on one evening during the school and is included in the registration. \n\n\n\nFor those seeking alternative options\, Liverpool offers a wide range of hotels\, serviced apartments\, and budget accommodations within easy reach of the University. \n\n\n\nLiverpool is well-connected by rail\, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car\, parking is available at the Paddington Village Car Park\, located close to the University and the recommended hotel. \n\n\n\nAirports:\n\n\n\n\nLiverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.\n\n\n\nManchester Airport (MAN) – Around 1 hour by train or car\, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options.\n\n\n\n\n\n\n\n\n\n\n\n\nContact details:\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk. \n\n\n\n\n\n\n\n\nThe Organising Committee:\n\n\n\nAlchemy HubUniversity of LiverpoolDr. Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Gabriella PizzutoDr Xenofon EvangelopoulosProf. Alessandro TroisiMinh CaoDr Mengjia ZhuDr Xin Yang
URL:https://aichemy.ac.uk/event/winter-school-robotics-and-ai-for-materials-chemistry-2026/
CATEGORIES:Training School
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DTSTART;TZID=UTC:20261209T140000
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DTSTAMP:20260911T131427Z
CREATED:20260911T130511Z
LAST-MODIFIED:20260911T131427Z
UID:13853-1796824800-1796828400@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – December 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE9th December 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\nREGISTER HERE\n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nProf. Matthew Sigman – University of Utah \n\n\n\nTalk Title: TBC \n\n\n\nDr. Ben Honore – Imperial College London \n\n\n\nTalk Title: TBC \n\n\n\nFollowing the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Matthew SigmanMedicinal Chemistry\n\n\n\n\n\nDr. Ben HonorePostdoctoral researcher\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-december-2026/
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DTSTART;TZID=UTC:20270106T000000
DTEND;TZID=UTC:20270109T143000
DTSTAMP:20261009T095257Z
CREATED:20260730T130724Z
LAST-MODIFIED:20261009T095257Z
UID:11569-1799193600-1799505000@aichemy.ac.uk
SUMMARY:Computational Methods for Defects in Solids Workshop
DESCRIPTION:KEY DETAILS\n\n\n\n\nSTART DATE/TIME6 January 12:00 END DATE/TIME8 January 14:30  COST£195 standard registration | £90/night college accommodation (limited places) REGISTRATION DEADLINE16 December 2026 (earlier if capacity reached). \n\n\n\nRegister Now \n\n\n\nAgenda (coming soon)\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nBMS Lecture Theatre\, Yusuf Hamied Department of Chemistry\, University of CambridgeLensfield Road\, Cambridge CB2 1EW\, United Kingdom. \n\n\n\nGoogle Maps Here\n\n\n\n\n\n\n\n\nOverview\n\n\n\nThis three-day workshop will gather established experts and early-career researchers for a focused discussion of the latest theoretical and computational advances for the modelling of defects in solids. Areas of interest include point and extended defects across bulk and low-dimensional solids\, AI/ML techniques for defect simulations\, dynamic defect processes\, many-body effects and excited states\, as well as pertinent challenges for advancing the field. \n\n\n\nA core goal of this workshop is to facilitate deep and insightful discussions\, aiming to establish crucial challenges and the near-term outlook for computational defect research\, strengthen collaboration in this growing worldwide community\, and impart a broad understanding of the state of the field for early-stage researchers. To achieve this\, we will favour shorter presentation slots with extended question and discussion time\, round-table panel discussions with all session speakers\, and dedicated question time for early-career researchers (ECRs). \n\n\n\nInvited keynote talks from internationally recognised leaders will set the stage at the beginning of each session\, followed by contributed talks and discussion. Two poster sessions with refreshments will be held on the first two evenings\, and the workshop dinner will take place in a historic Cambridge college hall on the second evening. All talks and discussions will be recorded and\, where speakers consent\, shared online after the workshop. \n\n\n\nKey topics\n\n\n\n\nComputational software for defect modelling — with a particular focus on challenges\, reproducibility and future outlook.\n\n\n\nTheoretical and methodological advances — machine-learning approaches (machine-learned potentials\, informatics and agentic AI methods)\, high-throughput studies\, and embedding & beyond-DFT methods.\n\n\n\nApplications and case studies — including energy conversion\, catalysis\, quantum information and conductivity.\n\n\n\nChallenges at the cutting edge — defect databases for AI/ML\, charge featurisation for machine-learned potentials\, extended defects\, dynamic processes and excited states.\n\n\n\n\n\n\n\n\nWhy attend?\n\n\n\n\nHear about the state of the art in defect modelling from international leaders\, with extended discussion built into every session.\n\n\n\nPresent your own work — contributed talk slots and two dedicated poster sessions\, with active participation from all attendees.\n\n\n\nDiscussion-first format: round-table panels after each session\, engaging poster sessions and dinner in a historic Cambridge college hall.\n\n\n\nBuild collaborations across the worldwide computational defects community\, in one place at one time.\n\n\n\nAffordable: subsidised registration includes all lunches\, coffee breaks\, poster receptions and the college-hall workshop dinner\, with optional low-cost college accommodation.\n\n\n\n\nWho should attend\n\n\n\nPhD students\, postdoctoral researchers\, academics and industry scientists working on (or moving into) the simulation of defects in materials; from method and software developers to those applying defect calculations in solid-state materials for energy\, catalysis\, quantum technologies and beyond. Experimentalists who want a working understanding of what modern defect simulations can (and cannot) deliver are also very welcome. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Sir Richard Catlow \n\n\n\n\n\nDr. Joel Davidsson\n\n\n\n\n\nDr. Irea Mosquera-Lois\n\n\n\n\n\nProf. Elif Ertekin\n\n\n\n\n\nDr. Menglin Huang\n\n\n\n\n\nDr. Katherine Inzani \n\n\n\n\n\n\n\nProf. Arkady V. Krasheninnikov \n\n\n\n\n\nProf. David Scanlon\n\n\n\n\n\nProf. Alex Shluger\n\n\n\n\n\nDr. Gergo Thiering\n\n\n\n\n\nDr. Mark Turiansky\n\n\n\n\n\nProf. Julia Wiktor \n\n\n\n\n\n\n\n\n\nSpeakerAffiliationTalk titleProf. Sir Richard CatlowUniversity College London and Cardiff UniversityA Historical Perspective on Defect ModellingDr. Joel DavidssonLinköping University\, SwedenData-Driven Exploration of Point Defects for Quantum TechnologiesDr. Irea Mosquera-LoisCuspAI\, London\, UKMachine learning for modelling defects under operating conditionsProf. Elif ErtekinUniversity of Illinois Urbana-Champaign\, USABeyond Isolated Defects: Predicting Configurations\, Dopability\, and Carrier Concentrations in Disordered MaterialsDr. Menglin HuangFudan University\, ChinaMachine-Learning Approaches for Multiphonon Transitions at DefectsDr. Katherine InzaniUniversity of Nottingham\, UKModelling Spin-Based Defects in Solids: From Electronic Structure to QubitsProf. Arkady V. KrasheninnikovHelmholtz-Zentrum Dresden-Rossendorf\, GermanyPoint and extended defects in 2D materials: insights from multi-scale atomistic calculationsProf. David ScanlonUniversity of Birmingham\, UKStructure searching for low energy defectsProf. Alexander ShlugerUniversity College London\, UKDefects in amorphous solids — do they exist and how to find themDr. Gergo ThieringWigner Research Centre for Physics\, HungaryAb-initio theory of orbital and phonon-driven relaxation pathways in quantum defects of semiconductorsDr. Mark TurianskyU.S. Naval Research Laboratory\, USAUnderstanding how defects interact with the latticeProf. Julia WiktorChalmers University of Technology\, SwedenTowards Realistic Modelling of Charge Localization in Solids\n\n\n\n\n\nRegistration & fees\n\n\n\n\nStandard registration – £195: includes attendance at all sessions\, lunches\, coffee breaks\, both catered poster sessions and the workshop formal dinner  at Jesus College\, Cambridge.\n\n\n\nCollege accommodation – £90/night: En-suite bed & breakfast accommodation at a Cambridge college. Places limited and allocated first-come\, first-served.\n\n\n\nBookings now open and close Wednesday 16 December 2026.\n\n\n\n\nAbstract submission\n\n\n\nWe welcome abstracts for contributed talks (15 minutes) and posters from all career stages; early-career researchers are particularly encouraged to submit. \n\n\n\nContributed Talk Abstract Deadline – Monday 23rd November 2026 (decisions by Monday 30th November) \n\n\n\nPoster Submission Deadline same as Registration Deadline – Wednesday 16th December 2026. \n\n\n\nAbstract Submission Online Form\n\n\n\n\n\nAccommodation & travel\n\n\n\n\nThe workshop is held before the start of the university teaching term\, allowing us to offer discounted college accommodation (single en-suite\, bed & breakfast). Places are limited\, so book early!\n\n\n\nPlenty of hotel and guesthouse options are also available in/near Cambridge city centre\, within walking distance of the venue.\n\n\n\nGetting to Cambridge: direct trains from London King’s Cross (~50 min) and London Liverpool Street; ~30 min by train or taxi from London Stansted Airport. The department is a ~15-minute walk (or short taxi/bus ride) from Cambridge train station.\n\n\n\n\nInclusion\, bursaries & code of conduct\n\n\n\n\nA limited number of registration fee waivers (for researchers from disadvantaged institutions) and childcare support bursaries will be available. Please contact the organisers at sk2045@cam.ac.uk to register interest in this.\n\n\n\nAll participants will be expected to follow the event code of conduct – AIchemy code of conduct\n\n\n\nTalks and discussions will be recorded; recordings will be shared online only where speakers consent. Please contact the organisers if you do not consent to this\, or if you have any accessibility requirements we should be aware of – the venue has step-free access.\n\n\n\n\n\n\nThe Organising Committee:\n\n\n\nOrganiserAffiliationDr. Seán Kavanagh (lead) Yusuf Hamied Department of Chemistry\, University of CambridgeProf. Keith McKenna Department of Physics\, University of YorkDr. Alexander SquiresCuspAI\, Cambridge\, UKProf. Samuel MurphySchool of Engineering\, Lancaster University\n\n\n\nWith administrative and event support from the AIchemy Hub team (Aysel Sarzosa and Chris Mellor).  \n\n\n\nFor questions related to this event please contact Dr. Seán Kavanagh.  \n\n\n\nSponsors:\n\n\n\nThis workshop is generously supported by Psi-k\, AIchemy\, and the UK Materials Chemistry Consortium (MCC)\, and co-organised with the Lennard Jones Centre (LJC) at Cambridge.
URL:https://aichemy.ac.uk/event/computational-methods-for-defects-in-solids-workshop/
CATEGORIES:Workshop
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