BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//aichemy - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://aichemy.ac.uk
X-WR-CALDESC:Events for aichemy
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20250101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20260311T000000
DTEND;TZID=UTC:20260415T235959
DTSTAMP:20260611T192623
CREATED:20260209T105627Z
LAST-MODIFIED:20260518T102722Z
UID:7430-1773187200-1776297599@aichemy.ac.uk
SUMMARY:Patenting AI & Materials: IP Webinars
DESCRIPTION:KEY DETAILS\n\n\n\n\nWEBINAR 1: IP FUNDAMENTALS DATE & TIME11 march 2026\, 14:00 – 15:00 WEBINAR 2: STRATEGIC IP DATE & TIME15 APRIL 2026\, 14:00 – 15:00 \n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nWEBINAR 1: IP FUNDAMENTALS WEBINAR 2: STRATEGIC IP \n\n\n\n\n\n\n\n\nJoin Keltie LLP patent attorneys Dr Monica Patel and Dr Emily Weal for a two-part webinar series on protecting innovation at the intersection of AI and materials science. The sessions will guide researchers\, innovators and start-ups through IP fundamentals\, patenting strategies\, and practical tools for recognising and protecting commercially valuable ideas in AI-enabled materials discovery. \n\n\n\nWEBINAR 1: IP FUNDAMENTALS \n\n\n\nThis webinar will introduce the fundamentals of IP for researchers and innovators working at the intersection of AI and materials. The session will cover the differences between patents\, registered designs and trade marks\, how the patent process works in the UK and internationally\, and what typical hurdles to patentability look like in practice. The session will showcase real examples of patentable technologies in materials science and AI\, and highlight how AI-driven approaches are being applied to materials discovery and development. The webinar is designed for a broad audience\, and no prior knowledge of IP or patents is required. \n\n\n\nWEBINAR 2: STRATEGIC IP \n\n\n\nThis webinar will build on these foundations to focus on how to recognise and protect commercially valuable ideas in AI and materials. The session will cover how to identify patentable inventions in your research\, principles of strategic patent drafting for data-driven and AI-enabled materials innovations\, and common IP ownership and collaboration pitfalls in multi-partner projects. The session will also cover an introduction to competitor patent searching and patent landscaping techniques\, and practical IP tips tailored for start-ups and spin-outs emerging from the AI and materials ecosystem. While open to all\, attendees will benefit from having joined Webinar 1 or having a basic familiarity with core IP concepts. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nDr Monica PatelSenior Associate\, Keltie LLP\n\n\n\n\n\nDr Emily WealPartner\, Keltie LLP
URL:https://aichemy.ac.uk/event/patenting-ai-materials-ip-webinars-keltiellp/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/02/IP-Webinars.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260402T100000
DTEND;TZID=UTC:20260402T170000
DTSTAMP:20260611T192623
CREATED:20260120T173007Z
LAST-MODIFIED:20260518T155021Z
UID:6577-1775124000-1775149200@aichemy.ac.uk
SUMMARY:Machine Learning for Experimental Materials Data
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE2nd April 2026 TIME10:00 – 17:00 Registration fee£0 – IOP & Associate Member      £15 – Student Non-Member £30 – Non-Member Registration Deadline20th March 2026 \n\n\n\nRegisteration now closed\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\ninstitute of physics37 Caledonian Rd\, London N1 9BU \n\n\n\n\n\n\n\n\nPlease note: This event has now passed \n\n\n\nJoin our IOP–AIchemy co-sponsored mini-symposium on machine learning for experimental materials data. This event will unite experimentalists and computational scientists to identify shared challenges\, exchange practical solutions\, and showcase how ML accelerates analysis across scattering\, microscopy\, tomography\, and spectroscopy; driving faster\, more insightful materials discovery. \n\n\n\nThis event is open to everyone\, whatever your background — whether you’re a practising physicist or simply curious to explore the subject. \n\n\n\n\n\nSpeakers:\n\n\n\n\nSam Cooper\, Imperial College London\n\n\n\nKim Jelfs\, Imperial College London\n\n\n\nAndrew McClusky\, University of Bristol\n\n\n\nDylan Owen\, University of Birmingham\n\n\n\nRob Palgrave\, University College London\n\n\n\nShijing Sun\, University of Cambridge\n\n\n\nSarah Haigh\, University of Manchester\n\n\n\nShelley Conroy\, Imperial College London \n\n\n\n\n\n\nPoster Abstracts \n\n\n\nWe invite contributions for poster presentations. If you are interested in presenting a poster\, please submit a short abstract (max 250 words) by email to claire.garland@iop.org by 20 March 2026. Posters will be A0 in portrait orientation. \n\n\n\n\n\nOrganised by:\n\n\n\n\nKelvin Wong\, Post Graduate Teaching Assistant – University College London\n\n\n\nKeith Butler\, Associate Professor in Computational Materials Chemistry – University College London\n\n\n\nAron Walsh – Professor at Imperial College & CSO at CuspAI
URL:https://aichemy.ac.uk/event/machine-learning-for-experimental-materials-data/
CATEGORIES:Symposium,Workshop
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/01/Machine-Learning-for-Experimental-Materials-Data-ft-image-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260413T000000
DTEND;TZID=UTC:20260417T235959
DTSTAMP:20260611T192623
CREATED:20251027T135314Z
LAST-MODIFIED:20251114T123044Z
UID:5736-1776038400-1776470399@aichemy.ac.uk
SUMMARY:CaMMLs - Chemical and materials machine learning school 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE & TIME13th April – 17th April 2026 COST£250 APPLICATION DEADLINE26th November 2025 \n\n\n\napply Here\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nSTFC Daresbury LabKeckwick Ln\, Daresbury\, Warrington WA4 4AD \n\n\n\n\n\n\n\n\nDescription\n\n\n\nThis machine learning for materials training course is being run by AIchemy Hub in collaboration with Physical Sciences Data Infrastructure (PSDI) initiative with support from STFC-SCD\, PSDS\, CCP5 and CCP9. 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. \n\n\n\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\nLearning outcomes\n\n\n\n\nAwareness of the state-of-the-art methods for machine learning for atomistic and molecular simulations\n\n\n\nHands on experience of using machine learning for atomistic and molecular simulations\n\n\n\n\nOutline Agenda – Draft\n\n\n\nSessionsMondayTuesdayWednesdayThursdayFridayMorning  DescriptorsNNsMLIPs generalGenerative modelsAfternoonIntro to MLBayesian OptimisationGNNsMLIPs – molecules/ materials EveningPostersResearch Seminar – Ruby Sedgwick – XymeBBQResearch Seminar – Venkat Kapil – UCL \n\n\n\nPre-requisites\n\n\n\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.  \n\n\n\nTimelines & Fees\n\n\n\nThe application deadline is 26th November 2025. Supervisors will be contacted for a letter of support following you application. All letters of support must be submitted by 6th December 2025. \n\n\n\nYou will be informed of the outcome of your application on 19th December 2025\, you will have to accept your place by 15th January 2026 and payment is required by 13th February 2026.  \n\n\n\nFood and 4 nights accommodation (Travelodge Warrington) is included in the £250 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.  \n\n\n\nPlease 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\nThe Organising Committee:\n\n\n\nAlchemy HubCaMMLSDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr. Chris Mellor (Imperial)Aysel Sarzosa Llerena (Imperial)Alin-Marin Elena (Scientific Computing Department\, STFC) Keith Butler (University College London)Reinhard Maurer (University of Vienna)Alex Ganose (Imperial)Ioan-Bogdan Magdău (Newcastle University)Nicola Knight (University of Southampton)
URL:https://aichemy.ac.uk/event/cammls-chemical-and-materials-machine-learning-school-2026/
CATEGORIES:Training School
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2025/11/CAMMLS.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260422T140000
DTEND;TZID=UTC:20260422T150000
DTSTAMP:20260611T192623
CREATED:20260325T142551Z
LAST-MODIFIED:20260508T151018Z
UID:8490-1776866400-1776870000@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – April 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE22nd April 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\nAI-Driven Experiments and Open-Source Automation for Accelerated Soft Matter Research Developing sustainable separation processes with AI \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. Lilo D. Pozzo – University of Washington \n\n\n\nTalk Title: AI-Driven Experiments and Open-Source Automation for Accelerated Soft Matter Research \n\n\n\nArtificial intelligence (AI)\, when paired with accessible laboratory automation\, can greatly accelerate materials optimization and scientific discovery. For example\, it can be used to efficiently map a phase-diagram with intelligent sampling along phase boundaries\, or in ‘retrosynthesis’ problems where a material with a target structure is desired but a synthetic route is not known. These approaches are especially promising in soft matter systems\, including block copolymer self-assembly\, nanoparticle synthesis\, and controlled colloidal assembly. In these systems\, design parameters (e.g. chemical composition\, MW\, topology\, processing) are vast\, history-dependent metastable and ‘out-of-equilibrium’ structures are common\, and functional properties are intimately tied to molecular design features and processing conditions. In addition\, for AI algorithms to operate efficiently in these spaces\, they must be ‘encoded’ with domain expertise specific to the problems being tackled. This talk will cover recent advances in accelerated materials research involving polymeric and soft-matter systems including dispersions and colloids. It will also outline remaining challenges and future opportunities. \n\n\n\nShort Biosketch: \n\n\n\nProf. Pozzo’s research interests are in the area of colloids\, polymers and soft-matter systems. Her research group focuses on controlling and manipulating materials structure for applications in healthcare\, alternative energy and sustainability. Her group also develops and utilizes laboratory automation and artificial intelligence (AI) to accelerate the development time-scales of new materials and applies advanced techniques based on neutron and x-ray scattering to characterize their nanostructure. Prof. Pozzo obtained her B.S. from the University of Puerto Rico at Mayagüez and her PhD in Chemical Engineering from Carnegie Mellon University in Pittsburgh PA. She also worked at the NIST Center for Neutron Research as a post-doctoral fellow and is currently the Boeing-Roundhill Chair Professor of Chemical Engineering at the University of Washington where she has served since 2007. She has been recognized with awards such as the Early Career Award from the Department of Energy\, the Clean Energy Empowerment and Education Award (C3E) from DOE\, and the Anne Mayes Award from the Neutron Scattering Society of America (NSSA). In addition to her research activities\, she is also dedicated to improving engineering education with course development in areas of entrepreneurship and service-oriented global engagement. \n\n\n\nJiyizhe Zhang – The University of Manchester \n\n\n\nTalk Title: Developing sustainable separation processes with AI \n\n\n\nChemical separations have long been essential to human society\, yet the separation of complex mixtures often remains lengthy and costly. Liquid-liquid extraction\, as a separation technology\, has wide applications in pharmaceuticals\, bioprocessing\, critical mineral recovery\, and nuclear waste treatment. Despite its widespread use\, many of the underlying physicochemical phenomena in liquid-liquid systems are not fully understood\, and the process development still relies heavily on shake-flask experiments as decades ago. This talk will present emerging technologies to accelerate separation process development through artificial intelligence\, automation\, and process modelling. Key challenges and future opportunities for digitalising separation science will be discussed. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Lilo D. Pozzo Professor of Chemical Engineering\n\n\n\n\n\nJiyizhe Zhang Lecturer in Chemical Engineering\n\n\n\n\n\nTahereh Nematiaram – Webinar Chair Chancellor’s Fellow
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-april-2026/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/03/April-Webinar-2026-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260513T090000
DTEND;TZID=UTC:20260513T180000
DTSTAMP:20260611T192623
CREATED:20260211T154100Z
LAST-MODIFIED:20260512T150610Z
UID:7352-1778662800-1778695200@aichemy.ac.uk
SUMMARY:Applications of AI for Catalysis and Energy Materials Discovery
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE13th May 2026 TIME09:00 – 18:00 COSTFree to Attend \n\n\n\nbook now\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nMolecular Sciences Research Hub (MSRH)\,  Imperial College London82 Wood Ln\, London W12 0BZ \n\n\n\n\n\n\n\n\nJoin us for a one-day symposium bringing together researchers working in catalysis and energy materials with AI experts developing and applying machine learning methods in this space.  \n\n\n\nThe programme features talks from leading researchers across chemistry\, materials science\, and AI\, showcasing real-world applications of data-driven approaches in catalysis\, interfaces\, electrolytes\, membranes\, and energy materials. \n\n\n\nThe day will combine invited talks\, discussion\, and dedicated networking time\, providing opportunities to share ideas\, learn about emerging methods\, and spark new interdisciplinary collaborations across experimental\, computational and machine learning research in the area of catalysis and energy materials. \n\n\n\nWho Should Attend:\n\n\n\nThis symposium is designed for: \n\n\n\n\nAcademics and researchers in catalysis\, interfaces\, and energy materials who are interested in applying AI and machine learning to enhance\, accelerate\, or expand their research.\n\n\n\nAI and machine learning experts looking to apply novel computational approaches to challenges in catalysis\, reaction engineering\, molecular discovery\, and energy‑relevant materials.\n\n\n\n\nWhether your work is experimental\, theoretical\, computational\, or method‑development‑focused\, this event offers a unique opportunity to build connections across disciplines and gain insights into the rapidly evolving intersection of AI\, catalysis\, and materials discovery. \n\n\n\n\n\n\n\n\nAgenda\n\n\n\nTimeSession09:00 – 09:30Registration and Coffee09:30 – 09:40Welcome & Opening Remarks – Maryam Mansoori Kermani (Imperial)Chair: Carla Molteni (KCL)09:40 – 10:15Speaker: Evgeny A. Pidko (Delft University of Technology)Phase Boundaries under CO₂ Hydrogenation Conditions on Ni-Ga Catalyst: Coincidence? I Think Not!10:15 – 10:50Speaker: Fernanda Duarte (University of Oxford)Reaction Modelling in the Age of Machine Learning10:50 – 11:20Coffee BreakChair: Chun Ann Huang (Imperial)11:20 – 11:55Speaker: Ricardo Grau Crespo (Queen Mary University of London)Designing materials for thermoelectric applications: density functional theory and machine learning11:55 – 12:30Speaker: James Durrant (University of Oxford) Optical Spectroscopic Analysis of Metal Oxide: Electrolyte Interfaces for Photo- and Electro-Catalysis12:30 – 13:30Lunch BreakChair: Shijing Sun (Cambridge)13:30 – 14:05Speaker: James A. Dawson (Newcastle University) Interfacial Design in Next-Generation Solid Electrolytes14:05 – 14:40Speaker: Qilei Song (Imperial) Molecular Engineering of Polymer Membranes for Sustainable Separation and Energy Processes14:40 – 15:10Coffee Break                      Chair: Sam Cooper (Imperial)15:10 – 15:45Speaker: Jacqueline Cole (University of Cambridge)Data-driven Materials Science for Energy-Sustainable Applications15:45 – 16:20Speaker: Maria Lukatskaya (ETH Zürich)Engineering Local Chemical Environments in Electrolytes for Efficient Batteries16:20 – 16:30Closing remark by Jarvist Frost (Imperial)16:30Networking\n\n\n\n\n\nSpeakers:\n\n\n\n\nProf. Evgeny Pidko – Delft University of Technology (TU Delft)\n\n\n\nProf. Fernanda Duarte – University of Oxford\, Department of Chemistry\n\n\n\nProf. Jacqui Cole – University of Cambridge\n\n\n\nDr. James Dawson – Newcastle University\n\n\n\nProf. James Durrant – University of Oxford\n\n\n\nProf. Maria Lukatskaya – ETH Zürich\n\n\n\nDr. Ricardo Grau‑Crespo – Queen Mary University of London\n\n\n\nDr. Qilei Song – Imperial College London\n\n\n\n\n\n\nTalk Abstracts:\n\n\n\nDesigning materials for thermoelectric applications: density functional theory and machine learning \n\n\n\nDesigning high-performance thermoelectric materials requires optimising a delicate balance of electronic transport\, lattice thermal conductivity\, and chemical stability. This talk will introduce a combined density functional theory (DFT) and machine-learning (ML) framework for accelerating this search. I will first outline how advanced first-principles workflows\, including anharmonic force-constant modelling and Boltzmann transport calculations\, provide detailed insight into carrier transport and heat flow in chalcopyrite compounds and related materials. I will then show how ML-based models such as hiPhive and CraTENet can dramatically reduce computational cost\, enabling rapid prediction of electronic and thermal transport properties across very large chemical spaces. Finally\, I will discuss how composition-driven screening coupled with generative AI crystal structure prediction techniques might open new routes for discovering Earth-abundant\, high-zT thermoelectrics. \n\n\n\nOptical Spectroscopic Analysis of Metal Oxide: Electrolyte Interfaces for Photo- and Electro-Catalysis  \n\n\n\nI will start by discussing the potential importance of both photoelectrocatalytic and electrocatalytic strategies in our transition to more sustainable energy systems. I will consider both the importance of water splitting to yield molecular hydrogen\, and also the wider challenges of CO2 and nitrogen reduction. A key challenge is eluciation of the materials design requirements for efficient catalytic function. I will go on to discuss the use of optical spectroscopies to provide insight into (photo)electrocatalytic function\, employing water oxidation as our most widely studied exemplar. In particular I will discuss how operando optical spectroelectrochemistry can used to determine redox state population densities and kinetics in metal oxides electrodes and photoelectrodes during water oxidation. This spectroelectrochemical approach is based on the idea that the redox states of most transition metal oxides are coloured\, allowing the specific concentrations of each state to be tracked by their optical absorption/reflection as a function of material\, applied bias\, time\, electrolyte etc. Such optical data can be complimentary to more widely employed electrochemical analyses such as J/V plots and Tafel analyses. In my talk I will discuss examples of the insights gained from operando spectroelectrochemistry into the design and function of a selection of materials\, including Iridium Oxide and Nickel/Iron Oxyhydroxide electrocatalysts\, as well as Hematite\, Bismuth vanadate and Strontium Titanate photoelectrodes/photocatalysts. \n\n\n\nInterfacial Design in Next-Generation Solid Electrolytes \n\n\n\nInterfaces are central to the performance\, functionality and longevity of energy materials and devices\, whether they be batteries\, fuel cells or a plethora of other crucial technologies. In solid-state batteries\, interfaces govern how efficiently ions move between the solid electrolyte and the electrodes. Poorly engineered interfaces can lead to high resistance\, reduced performance and even instability\, while well-designed interfaces enhance conductivity\, battery life and safety. In the first part of this talk\, I will discuss how machine learning interatomic potentials have enabled us to explore interfaces and ion transport at scales far beyond the capabilities of ab initio molecular dynamics. This includes the investigation of solid electrolyte interphase formation between argyrodite Li6PS5Cl and Li metal and universal cation transport in LaCl and LaBr-based solid electrolytes. In the second part of this talk\, I will present our recent atomistic simulations of grain boundaries in topical solid electrolytes\, including garnet Li7La3Zr2O12 and argyrodite Li6PS5Cl. Specifically\, I will discuss how the different electronic structures of Li7La3Zr2O12 and Li6PS5Cl grain boundaries lead to fundamentally different mechanisms of lithium ion incorporation and reduction and lithium atom nucleation in these two materials. \n\n\n\nData-driven Materials Science for Energy-Sustainable Applications \n\n\n\nThis lecture presents the development and application of data-extraction tools andmachine-learning (ML) models that are enabling data-driven materials science forenergy-sustainable applications. The talk describes how to source high-quality data tobuild custom databases and apply them to (a) train ML models; (b) realise data-drivenmaterials discovery; (c) conduct data-driven materials science for the energy sector; (f)create energy-efficient domain-specific language models for materials science to feedagentic AI and help democratise AI for the materials research community. The casestudies presented track a theme of energy-sustainable materials \n\n\n\nEngineering Local Chemical Environments in Electrolytes for Efficient Batteries \n\n\n\n“Electrolytes play a crucial role in energy storage devices\, impacting their environmental footprint\, safety\, cost\, and performance. This talk will cover two areas of electrolyte research for improving batteries. \n\n\n\nFirst\, we will explore water-based batteries. Aqueous electrolytes\, being non-flammable and less toxic\, offer safer battery operation. However\, their limited electrochemical stability window reduces energy density. To address this\, highly concentrated “”water-in-salt”” (WIS) electrolytes have been developed\, significantly expanding the stability window and enhancing the performance of Li-ion and Zn metal batteries for grid energy storage. Despite their advantages\, WIS electrolytes have high viscosity and require large amounts of potentially toxic salts\, which limits their usability. We will discuss how cation solvation\, electrolyte structure\, and hydrogen bonding influence the electrochemical properties and performance\, particularly in Zn plating/stripping and electrolyte decomposition. Additionally\, we will explore strategies for engineering relatively dilute electrolytes for efficient aqueous batteries. \n\n\n\nSecond\, the talk will present a novel method for stabilizing interfaces in Li metal batteries (LiMBs). Conventional electrolytes result in low cycle life and safety issues due to “”dead”” lithium and dendrite formation. Prior research suggests that fluorine-rich interfacial layer chemistry is important for the stabilization of Li-metal anodes\, which can be achieved when electrolytes with a high fraction of fluorinated solvents and/or salts are used. We propose an alternative approach using electrostatic attraction between positively charged readily reducible fluorinated cations and the negatively charged anode. This method enables the formation of a robust fluorine-rich interfacial layer with minimal additive (as low as 1 mM) facilitating dense\, conformal Li deposition. This strategy can offers a cost-effective\, environmentally friendly solution for enhancing high-energy batteries.” \n\n\n\n\n\nContact details:Aysel Sarzosa (asarzosa@ic.ac.uk) or Chris Mellor (c.mellor@imperial.ac.uk). \n\n\n\nIn addition\, if you have caring responsibilities that may affect your ability to attend\, take a look at our Conference Care Fund and get in touch to see how we can support you to attend. \n\n\n\n\n\n\n\n\n\nOrganised by:\n\n\n\nDr Maryam Mansoori Kermani\, MSCA Fellow at Imperial College London with the support of AIchemy Hub – Project Team.
URL:https://aichemy.ac.uk/event/applications-of-ai-for-catalysis-and-energy-materials-discovery/
CATEGORIES:Symposium,Workshop
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/02/Applications-of-AI-for-Catalysis-and-Energy-Materials-Discovery-Symposium.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260520T140000
DTEND;TZID=UTC:20260520T150000
DTSTAMP:20260611T192623
CREATED:20260508T151957Z
LAST-MODIFIED:20260518T105820Z
UID:9410-1779285600-1779289200@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – May 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE20th May 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. Bao Nguyen – University of Leeds \n\n\n\nTalk Title: Who’s learning from whom? Beyond the black boxes of chemical models. \n\n\n\nArtificial intelligence and machine learning are now central tools for chemists seeking to predict molecular properties and reaction outcomes. Yet as these models grow increasingly sophisticated\, their inner workings often remain opaque\, and the chemical data they rely on—like all experimental data—can be noisy\, sparse\, or biased. In this talk\, Bao will illustrate how we address these challenges in the context of solubility prediction: from handling imperfect datasets to building models that both perform robustly and provide trustworthy predictions on previously unseen data. \n\n\n\nHe will then show how the usual paradigm can be reversed. Rather than using algorithms solely to predict the results of complex reactions\, we can use the data generated through Bayesian Optimisation to reveal mechanistic insights that would otherwise remain hidden. This shift—from prediction to understanding—opens new opportunities for rationally tackling selectivity problems in modern synthetic chemistry. \n\n\n\nNikola Radulov – University of Liverpool \n\n\n\nTalk Title: FLIP: Flowability-Informed Powder Weighing \n\n\n\nAutonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow\, coupled with variability in laboratory conditions necessitates adaptive automation. We introduce FLIP\, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. The core of the framework lies in using material flowability\, quantified by the angle of repose\, to optimise physics-based simulations through Bayesian inference. This yields material-specific simulation environments capable of generating accurate training data\, which reflects diverse powder behaviours\, for training “robot chemists”.  We demonstrate how FLIP integrates quantified flowability into a curriculum learning strategy\, fostering efficient acquisition of robust robotic policies by gradually introducing more challenging\, less flowable powders. We validate the efficacy of our method on a robotic powder weighing task under real-world laboratory conditions. Experimental results show that FLIP with a curriculum strategy achieves a low dispensing error of 2.12 +/- 1.53 mg\, outperforming methods that do not leverage flowability data\, such as domain randomisation (6.11 +/- 3.92 mg). These results demonstrate FLIP’s improved ability to generalise to previously unseen\, more cohesive powders and to new target masses.Following the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Bao Nguyen Physical Organic Chemistry\n\n\n\n\n\nNikola RadulovEarly Career Research\n\n\n\n\n\nDr. Adam ClaytonAssociate Professor
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-may-2026/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/jpeg:https://aichemy.ac.uk/wp-content/uploads/2026/05/May-Webinar-2026.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260602T090000
DTEND;TZID=UTC:20260602T170000
DTSTAMP:20260611T192623
CREATED:20260220T114907Z
LAST-MODIFIED:20260601T163522Z
UID:7844-1780390800-1780419600@aichemy.ac.uk
SUMMARY:ECR Exchange: Scotland 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE2 JUNE 2026 TIME09:00 – 17:00 COSTFREE ABSTRACT SUBMISSION DEADLINE5 MAY 2026 REGISTRATION DEADLINE22 MAY 2026 \n\n\n\nSold out\n\n\n\nView Agenda\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF STRATHCLYDETechnology and Innovation Centre\, 99 George Street\,  Glasgow\, G1 1RD \n\n\n\n\n\n\n\n\nThe AIchemy ECR Exchange is primarily aimed at Early Career Researchers across the UK\, including PhD students and postdoctoral researchers working at the intersection of AI and Chemistry. While Scotland-focused\, the event welcomes participants from further afield who are keen to contribute to and engage with a growing research community. This Exchange is designed to connect emerging researchers\, create space for meaningful networking\, and encourage open knowledge sharing. By bringing together ECRs working across disciplines\, the event aims to spark new collaborations and lay the groundwork for future collaboration opportunities.Through engagement with industry speakers and partners\, attendees will gain insight into real-world challenges and translational pathways\, helping to shape and focus their research and future career development. \n\n\n\n\n\n\n\nCall for Lightning Talk Abstracts \n\n\n\nWe invite submissions of oral and poster abstracts from researchers working across all areas at the intersection of AI and Chemistry\, including interdisciplinary and applied research. This is an opportunity for ECR’s\, PhD students and postdoctoral researchers to showcase their work to a focused audience of peers\, academic leaders\, and industry representatives working at the AI and chemistry interface. Selected applicants will be invited to deliver an oral presentation as part of the main programme\, providing a platform to share their research\, receive feedback\, and spark potential collaborations. There will also be the opportunity to present a poster\, creating additional space for discussion\, networking\, and deeper engagement with attendees. Please note: Deadline for Submissions has now passed \n\n\n\n\n\n\n\nWho Should Attend: \n\n\n\nThe AIchemy ECR Exchange is primarily aimed at Early Career Researchers\, including: \n\n\n\n\nPhD students\n\n\n\nPostdoctoral researchers\n\n\n\nEarly Career Independent Academic\n\n\n\n\nThe event is relevant but not limited to those interested in the application or development of AI for accelerated chemistry and materials discovery. Participation is open to researchers from across the UK who are keen to collaborate and engage with Scotland’s research community. \n\n\n\nWhat you’ll gain: \n\n\n\n\nOpportunities to present and discuss their research\n\n\n\nDedicated networking to encourage future collaborations\n\n\n\nInsight from industry speakers on real-world challenges\n\n\n\nExposure to potential pathways for research impact and funding\n\n\n\n\n\n\nInvited Speakers\n\n\n\n\n\n\n\n\n\nDr Katerina VrizaPrincipal Scientist at GSK\n\n\n\n\n\nDr Joanne CookFormulation Manager at Unilever\n\n\n\n\n\nDr Laia Vila-NadalUniversity of Glasgow\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 StrathclydeDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Tahereh NematiaramDr Yashar Moshfeghi
URL:https://aichemy.ac.uk/event/ecr-exchange-scotland-2026/
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/05/New-featured-image-for-events-65.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260606T000000
DTEND;TZID=UTC:20260607T235959
DTSTAMP:20260611T192623
CREATED:20260515T151112Z
LAST-MODIFIED:20260515T151422Z
UID:9620-1780704000-1780876799@aichemy.ac.uk
SUMMARY:The Great Exihibition Road Festival 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE6-7 June 2026 TIME12.00 – 18.00 COSTFREE \n\n\n\nREGISTER Here\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nExhibition Road\, Great Exhibition Road Festival/Marquee – Prince’s GardensSouth Kensington Campus \n\n\n\n\n\n\n\n\nThe Great Exhibition Road Festival is a free annual celebration of science and the arts each summer in South Kensington.  \n\n\n\nCome visit our Candy Chemistry booth in the Family Fun Zone at the Great Exhibition Road Festival! \n\n\n\nJoin researchers from Imperial’s Departments of Chemical Engineering\, Chemistry\, and Computing to explore molecular structure using colourful edible models. Use marshmallows\, toothpicks and multicoloured gummies to build molecular structures\, then rotate them and see if you can make two structures that are mirror images of each other. \n\n\n\nDiscover why understanding molecular structure is essential and how AI helps scientists predict and design molecules that shape the world around us. \n\n\n\nJust like our left and right hands\, molecules may exist in left-handed and right-handed forms that look similar but behave very differently. Changing a molecule’s “handedness” (or chirality) can completely alter its properties\, from fragrance to pharmaceutical activity.
URL:https://aichemy.ac.uk/event/the-great-exihibition-road-festival/
ATTACH;FMTTYPE=image/jpeg:https://aichemy.ac.uk/wp-content/uploads/2026/05/Great-Exhibition-Road-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260617T140000
DTEND;TZID=UTC:20260617T150000
DTSTAMP:20260611T192623
CREATED:20260601T105940Z
LAST-MODIFIED:20260601T110255Z
UID:9961-1781704800-1781708400@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – June 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE17th June 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\nAssoc. Prof. Timonthy Cernak – University of Michigan \n\n\n\nTalk Title: Combinatorial explosion: from atom-bond arrangements to exotic diseases \n\n\n\nChemical synthesis and data science are two fields that operate in synergy. Molecules and the routes to synthesize them are easily represented as graphs while automated chemical synthesis strategies allow more and more synthesis data to be captured\, for instance to feed machine learning algorithms. This talk will detail our work in this area focused on a new class of amine-acid cross coupling reactions\, and the computer-assisted synthesis of drugs and natural products. We have been exploring the breadth of all reactions that could exist\, navigating combinatorial explosions of virtual and plausible reaction methods\, routes to complex molecules\, and the interconnectedness of reaction conditions\, transformations\, and biological functions. \n\n\n\nOur agnostic view of reactions and their mechanisms has recently extended to diseases\, with a focus on One Health. We aspire to produce medicines and treatments for health challenges in endangered species. We call this new area conservation chemistry\, and examples from the frontlines of this field and lab-based research will be shared. \n\n\n\nSimone Gallarati – University of Utah \n\n\n\nTalk Title: Enabling data-efficient strategies for asymmetric reaction optimization and ligand discovery \n\n\n\nIn order to optimize an asymmetric reaction\, machine learning (ML) models are frequently implemented to screen virtual libraries of chiral catalysts and identify candidates with superior performance. Unfortunately\, such models are often poorly transferable to new reactions involving a different combination of known substrate types or an entirely unfamiliar class of compounds. In this talk\, I will first introduce a descriptor generation strategy that accounts for possible changes in a reaction’s stereodetermining step with catalyst or substrate identity\, allowing us to model mechanistically complex transformations involving distinct ligand and substrate types. Our ML workflow has led to the optimization of poorly performing examples reported in a substrate scope and to accurate out-of-sample predictions on unseen ligand and reaction partners.1 \n\n\n\nOne limitation of inference-based ML models is the need for large virtual libraries of potential catalysts\, whose curation is frequently associated with significant computational costs. In the second part of the talk\, I will introduce a genetic algorithm-based pipeline2 whereby only a small population of ligands is computed and evaluated experimentally at each iteration of the optimization loop. This strategy leverages the modularity of catalyst scaffolds and is compatible with early reaction optimization campaigns\, requiring the featurization and synthesis of only small batches of ligands. Overall\, these workflows enable streamlined reaction development\, quantitatively transferring knowledge learned on sparse data sets to novel chemical spaces. \n\n\n\nReferences \n\n\n\n(1) Gallarati\, S.; Bucci\, E. M.; Doyle\, A. G.; Sigman\, M. S. Transferable Enantioselectivity Models from Sparse Data. Nature 2026\, 651\, 637–646. \n\n\n\n(2) Gallarati\, S.; van Gerwen\, P.; Schoepfer\, A. A.; Laplaza\, R.; Corminboeuf\, C. Genetic Algorithms for the Discovery of Homogeneous Catalysts. CHIMIA 2023\, 77 (1/2)\, 39.Following the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nAssoc. Prof. Timonthy CernakMedicinal Chemistry\n\n\n\n\n\nSimone Gallarati Postdoctoral 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-june-2026/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/06/June-Webinar-26.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260618T170000
DTEND;TZID=UTC:20260618T210000
DTSTAMP:20260611T192623
CREATED:20260407T072353Z
LAST-MODIFIED:20260528T140147Z
UID:8110-1781802000-1781816400@aichemy.ac.uk
SUMMARY:Science in the City: An Evening of AI
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE18 JUNE 2026 TIME17:00 – 21:00 COSTFREE \n\n\n\nbook now\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nTHE SPINE BUILDING2 Paddington Village\, Liverpool L7 3FA \n\n\n\n\n\n\n\n\nStep into the world of AI and see how it’s shaping science\, healthcare\, and innovation across Liverpool and the UK. From groundbreaking research at the University of Liverpool to real-world applications in our city region\, this event brings science to life for a community that takes pride in Liverpool’s innovation\, collaboration\, and world-class research. Meet researchers and take part in discussions with experts\, ask your questions\, share your ideas\, and see how AI is being used responsibly to make a positive impact. This event is open to everyone\, whether you’re a researcher\, city resident\, alumni or just curious about how AI is shaping the future. Enjoy a welcoming reception before and after the event\, the perfect time to meet fellow attendees\, spark conversations\, and celebrate Liverpool’s thriving research community.Please Note: Registration to this event is essential \n\n\n\n\n\n\n\nWhat you’ll gain: \n\n\n\n\nExplore how AI is driving breakthroughs in science.\n\n\n\nEngage directly with experts in Q&A sessions.\n\n\n\nCelebrate and support our city’s research excellence.\n\n\n\n\nNo prior experience in AI is required\, just curiosity and a desire to be part of the talent\, dedication\, and pride that Liverpool brings to the forefront of scientific discovery. \n\n\n\n\n\n\n\nEvening Programme: \n\n\n\n5:00 – 6:00pm | Arrival & Welcome ReceptionJoin us for a relaxed welcome with refreshments. Meet fellow attendees and start the conversation in an informal setting overlooking Liverpool’s vibrant skyline.6:00 – 8:00pm | AI Across the Sciences – Speaker Sessions & DiscussionHear from leading voices in AI and scientific research as they explore how artificial intelligence is advancing materials chemistry\, drug discovery\, healthcare\, and more. Each session will include opportunities for questions and audience interaction.8:00 – 9:00pm | Conversations & Community ReceptionContinue the discussion over refreshments. Connect with researchers\, alumni\, and partners\, and be part of the growing AI and science community shaping Liverpool’s future. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProfessor Andy CooperUniversity of Liverpool\n\n\n\n\n\nProfessor Katie AtkinsonUniversity of Liverpool\n\n\n\n\n\nDr Alex BattyClatterbridge Cancer Centre\n\n\n\n\n\nDr Gabriella PizzutoUniversity of Liverpool\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\, The Spine Building and the recommended hotel. \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 HubDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)
URL:https://aichemy.ac.uk/event/science-in-the-city-an-evening-of-ai/
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/05/New-featured-image-for-events-66.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260716T090000
DTEND;TZID=UTC:20260716T170000
DTSTAMP:20260611T192623
CREATED:20260505T135703Z
LAST-MODIFIED:20260519T140000Z
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\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\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
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/05/New-featured-image-for-events-51.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260721T090000
DTEND;TZID=UTC:20260721T170000
DTSTAMP:20260611T192623
CREATED:20260206T072607Z
LAST-MODIFIED:20260602T142707Z
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 LIGHTNING TALK SUBMISSION OPEN REGISTRATION DEADLINE3 JULY 2026 \n\n\n\nREGISTration Open\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
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/02/ai-for-chem-education-workshop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260901T090000
DTEND;TZID=UTC:20260903T170000
DTSTAMP:20260611T192623
CREATED:20260313T125151Z
LAST-MODIFIED:20260424T100655Z
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 DEADLINE26 JUNE 2026 \n\n\n\nApply Now\n\n\n\nagenda\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF LIVERPOOLMaterials Innovation Factory\, 51 Oxford St\, Liverpool\, L7 3NY \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 26th June 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
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/03/New-featured-image-for-events-42-e1774872685285.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260914T000000
DTEND;TZID=UTC:20260916T235959
DTSTAMP:20260611T192623
CREATED:20260512T131430Z
LAST-MODIFIED:20260518T055340Z
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\nAPPLY HERE\n\n\n\n agenda COMING SOON\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\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 – To be announced\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 3rd July 2026 and decisions will be given to applicants by 10th 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
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/05/New-featured-image-for-events-52.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260923T090000
DTEND;TZID=UTC:20260923T170000
DTSTAMP:20260611T192623
CREATED:20260502T120152Z
LAST-MODIFIED:20260529T063441Z
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\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF LEEDSDepartment of Chemistry\, Woodhouse Lane\, Leeds\, LS2 9JT \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 LeedsDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Professor Bao Nguyen
URL:https://aichemy.ac.uk/event/demystifying-agentic-ai-ai-agents-workshop/
CATEGORIES:Symposium
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/05/New-featured-image-for-events-50.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20261123T000000
DTEND;TZID=UTC:20261127T235959
DTSTAMP:20260611T192623
CREATED:20260220T124659Z
LAST-MODIFIED:20260515T160421Z
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\nAPPLY HERE\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\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\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\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 October 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 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
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/02/New-featured-image-for-events-26.png
END:VEVENT
END:VCALENDAR