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DTSTART;TZID=UTC:20260901T090000
DTEND;TZID=UTC:20260903T170000
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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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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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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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BEGIN:VEVENT
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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