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DTSTART;TZID=UTC:20251101T080000
DTEND;TZID=UTC:20260109T120000
DTSTAMP:20260412T171431
CREATED:20251104T103507Z
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SUMMARY:Catechol Benchmark Hackathon (NeurIPS 2025 DnB)
DESCRIPTION:ML model-building challenge for reaction yield prediction for catechol rearrangement reaction from transient flow dataset.\n\n\n\n\n\n\n\n\n\n\n\nKEY DETAILS\n\n\n\n\nLAUNCH DATE3rd November 8:00 AM CLOSING DATE9th January 12:00 PM \n\n\n\nVIEW COMPETITION ON kaggle\n\n\n\n\n\n\n\n\nOverview:\n\n\n\nWelcome to the Catechol Benchmark Hackathon competition!  \n\n\n\nIn this competition\, we will have multiple teams trying to prediction reaction outcomes of the rearrangement of allyl substituted catechol under different solvent and process conditions. \n\n\n\nThe data-set consists of multiple transient flow ramps\, which allow us to assess the amount of starting material and products after seeing the reaction at different temperatures and residence times (i.e. how long the chemicals reacted for). We also include many data-points for binary mixtures of solvents\, allowing us to treat the usually discrete solvent selection problem as a semi-continuous one. \n\n\n\nGoal: Build a machine learning model that achieves the best predictions on the collected data\, as measured by a cross-validation procedure\, which will demonstrate the ability of your model to predict on unseen solvent data.  \n\n\n\nDescription\n\n\n\nMore details of the data-set: \n\n\n\nData size and inputs\n\n\n\nThe data-set consists of 1227 data points on the allyl substituted catechol reaction\, covering 24 solvents at different temperatures and residence times. The inputs of the model will consist of: \n\n\n\n(1) A selection of two different solvents\, Solvent A and Solvent B\, with the corresponding amount of Solvent B in the mixture given by the percentage %B. \n\n\n\n(2) The temperature in °C at which the reaction was carried out. \n\n\n\n(3) The residence time of the reaction\, i.e.\, how long the reactants were subject to the reaction conditions applied. \n\n\n\nThe outputs consist of the yield of the starting material and the two observed products. We also created a smaller data set of 656 data-points in which solvent mixtures are not considered\, and only single solvent data\, along with residence times and temperatures is considered. \n\n\n\nEvaluation\n\n\n\nSubmissions will be evaluated according to a cross-validation procedure. This public notebook (https://www.kaggle.com/code/josepablofolch/catechol-benchmark-hackathon-template) shows the structure any submitted notebook must follow. In order to ensure fair participation among all competitors\, the submission must have the same last three cells as in the notebook template\, with the only allowed change being the line where the model is defined.  \n\n\n\nFor the avoidance of doubt\, the line  model = MLPModel() can be replaced with a new model definition in the third to last and second to last cells\, but everything else must remain the same. \n\n\n\nPrizes\n\n\n\nPrizes will be awarded on a per-person basis as follows: \n\n\n\nTotal Prizes Available: £2\,000 (GBP) \n\n\n\n\n1st Place – £250 per person (maximum £1000 total for a team of four)\n\n\n\n2nd Place – £150 per person (maximum £600 total for a team of four)\n\n\n\n3rd Place – £100 per person (maximum £400 total for a team of four)
URL:https://aichemy.ac.uk/event/catechol-benchmark-hackathon-neurips-2025-dnb/
CATEGORIES:Hackathon
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BEGIN:VEVENT
DTSTART;TZID=UTC:20251208T000000
DTEND;TZID=UTC:20251212T235959
DTSTAMP:20260412T171431
CREATED:20250823T075605Z
LAST-MODIFIED:20260223T111409Z
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SUMMARY:Winter School: Robotics and AI for Materials Chemistry 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE8 – 12 December\, 2025 COST£100 \n\n\n\nApplications CLOSED\n\n\n\nView detailed agenda\n\n\n\nRead the Blog\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nChemistry Building\, University of LiverpoolLiverpool\, L69 7ZD\, United Kingdom \n\n\n\n\n\n\n\n\nPlease note: Applications are now closed for this training school. \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\n\n\n\n\n\nProvisional Programme\n\n\n\nMondayTuesdayWednesdayThursdayFridayInvited Talk: Dr Felix HankeInvited Talk: Dr Michele CaprioPractical Tutorial: Bayesian Optimisation for Chemistry – Dr Bojana RankovicInvited Talk: Dr Efi Psomopoulou Invited Talk: Dr Shijing SunPractical Tutorial: Bayesian Optimisation Basics and Human-in-the-loop modelling’ – Dr Manisha DubeyPractical Tutorial: Robot SimulationPractical Tutorial: Robot ManipulationProject WorkInvited Talk: Prof. Subramanian RamamoorthyPractical Tutorial: Machine Vision for ChemistryProject WorkProject WorkProject WorkProject PresentationsSocial Networking Event\n\n\n\n\n\n\n\nConfirmed Speakers\n\n\n\n\n\nDr Felix HankeCusp AI\n\n\n\n\n\nDr Manisha DubeyUniversity of Edinburgh\n\n\n\n\n\nDr Michele Caprio University of Manchester\n\n\n\n\n\nDr Bojana RankovicEPFL\n\n\n\n\n\n\n\nDr Efi PsomopoulouUniversity of Bristol\n\n\n\n\n\nDr Shijing SunUniversity of Cambridge\n\n\n\n\n\nProf. Subramanian Ramamoorthy University of Edinburgh\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 October 2025 and decisions will be given to applicants by 31st October 2025. \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\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 Cao
URL:https://aichemy.ac.uk/event/winter-school-robotics-and-ai-for-materials-chemistry-2025/
CATEGORIES:Training School
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DTSTART;TZID=UTC:20251210T140000
DTEND;TZID=UTC:20251210T150000
DTSTAMP:20260412T171431
CREATED:20251125T132616Z
LAST-MODIFIED:20260325T135926Z
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SUMMARY:AIchemy’s Monthly Webinar Series - December 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE10 December 2025 TIME14:00 – 15:00 COSTFree \n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nLarge-scale Crystal Structure Prediction: Learning from 1\,000 molecules and beyond Retention Is All You Get (But Maybe It’s All You Need) \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. Keith Butler – University College of London \n\n\n\nTalk Title: Retention Is All You Get (But Maybe It’s All You Need): Using Large Language Models to Design and Discover New Materials \n\n\n\nLarge language models (LLMs) have transformed how we work with text\, but their underlying mechanism\, autoregressive next-token prediction\, naturally extends to any domain that can be expressed as a sequence. In this webinar\, Keith will explore how this paradigm can be repurposed for chemistry and materials science by treating crystal structures as a “language” and training LLMs to generate them. \n\n\n\nHe will discuss his recent work developing CrystaLLM\, an autoregressive model trained on large collections of crystallographic data. The model learns the statistical grammar of known materials well enough to generate syntactically valid and chemically plausible crystal structures. However\, detailed interrogation shows that the model’s apparent creativity is predominantly driven by retention\, recombining motifs seen in its training data rather than building a genuine\, generalisable “world model” of chemistry. This distinction is important for how such models are interpreted and deployed in discovery workflows. \n\n\n\nKeith will then introduce his team’s latest extensions using conditional generation\, which allow them to steer the model with property targets or experimental measurements. This approach does not magically endow the model with chemical reasoning\, but it provides a powerful way to exploit its learned structural priors. He will illustrate this with examples such as conditioning on X-ray diffraction patterns to accelerate structure solution and conditioning on target optoelectronic properties to bias generation toward functional materials spaces. \n\n\n\nOverall\, the aim of Keith’s talk is to provide a realistic\, scientifically grounded view of what LLMs can and cannot do for chemical discovery. These models are powerful tools for pattern learning and hypothesis generation\, but they do not yet constitute autonomous scientific reasoners. Understanding this helps researchers design workflows where they offer genuine advantage without overstating their capabilities. \n\n\n\nChris Taylor – University of Southampton \n\n\n\nTalk Title: Large-scale Crystal Structure Prediction: Learning from 1\,000 molecules and beyond \n\n\n\nComputational molecular crystal structure prediction (CSP) is a mature and powerful tool in materials discovery\, able to successfully predict and rank the possible crystal polymorphs of a range of functional materials at increasingly large scale. In this talk\, I describe our landmark study carrying out thorough CSP explorations on over 1\,000 rigid molecules with experimentally-known forms\, demonstrating our CSP workflow’s overwhelming success in predicting and ranking known forms\, and in rationalising empirical crystal engineering rules. I also demonstrate the potential of such large-scale data generation by presenting a machine-learned energy correction and a message-passing (MACE) neural network potential trained on this data\, as examples of the possibilities for employing AI trained on such datasets to empower functional materials discovery. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Keith ButlerAssociate Professor in Computational Materials Chemistry\n\n\n\n\n\nDr. Chris Taylor Postdoctoral Research Fellow\n\n\n\n\n\nJohn WardWebinar Host
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-december-2025/
CATEGORIES:Webinar
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