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BEGIN:VEVENT
DTSTART;TZID=UTC:20250219T140000
DTEND;TZID=UTC:20250219T150000
DTSTAMP:20260412T184014
CREATED:20241122T151601Z
LAST-MODIFIED:20250603T133054Z
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SUMMARY:AIchemy’s Monthly Webinar Series – February 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE19 February\, 2025 TIME14:00 – 15:00 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nData-driven Materials Discovery Machine Learning Potentials: Beyond Potential Energy \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. Fadwa El Mellouhi – Data-driven Materials Discovery \n\n\n\nThe webinar focuses on the prospects of Data-driven approaches in accelerating materials discovery. It is motivated by the upsurge of machine learning (ML) applications\, big data and the adoption of computer science tools in materials science. Fadwa will give an overview of the latest advances in this field with focus on advances made in the last few years applied to energy and environmental materials. Fadwa will show how the combination of high throughput density functional theory (DFT) calculation with machine learning can be useful to perform a systematic analysis of the structure-to- property relation enabling to explore different classes of materials. Fadwa will highlight how the approach offers an interesting guideline to engineer novel materials for light absorption\, green hydrogen production and CO2 reduction while enabling to reduce the huge space of experimental trial and error. \n\n\n\nRoss Urqurhart – Machine Learning Potentials: Beyond Potential Energy \n\n\n\nMachine learning potentials (MLPs) represent a transformative approach in computational chemistry\, combining the efficiency of molecular dynamics with the precision of quantum mechanical methods like density functional theory. While traditionally employed for predicting potential energy surfaces\, MLPs have the potential to go far beyond. In this talk\, I will explore how MLPs can be adapted to predict complex properties such as pKa values and reaction pathways\, highlighting their versatility in addressing challenges beyond conventional energy predictions. \n\n\n\nFollowing the presentations\, there will be time for questions from the audience. \n\n\n\nWe look forward to having you attend the event! \n\n\n\n\n\nSpeakers\n\n\n\n\n\nFadwa El MellouhiSenior Scientist\n\n\n\n\n\nRoss UrqurhartResearch Postgraduate\n\n\n\n\n\nKeith Butler – Webinar ChairAssociate Professor in Computational Materials Chemistry
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-february-2025/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250122T150000
DTEND;TZID=UTC:20250122T160000
DTSTAMP:20260412T184014
CREATED:20241122T150721Z
LAST-MODIFIED:20251113T153521Z
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SUMMARY:AIchemy’s Monthly Webinar Series – January 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE22 January\, 2025 TIME15:00 – 16:00 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nSo\, you want to build a self-driving lab? \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. Sterling Baird – So\, you want to build a self-driving lab? \n\n\n\nThe scientific community has been exploring how to utilize AI and automation to create “self-driving” (i.e.\, autonomous) scientific laboratories with the goal of accelerating the rate of scientific discovery (i.e.\, new materials and new phenomena). Like peering over the edge of a high-dive\, getting into the space of self-driving labs\, or even AI and automation in general\, can be daunting. Recently\, self-driving labs for chemistry and materials science have led to accelerated scientific discoveries related to climate change\, energy\, and medicine.  Each of these labs are usually built over the course of several years by interdisciplinary teams\, requiring capital in the millions. Of the reported self-driving labs\, many have only ever initiated a single “glory flight” campaign. Many naturally find themselves asking questions such as the following. What exactly is a self-driving lab? Where do I start? What are the benefits and risks? While the answers are highly dependent on circumstance and project visions\, we provide perspectives on the hardware\, software\, personnel\, and other requirements necessary to be sustainably successful in this space. This will cover topics such as robotics\, computer vision\, machine learning\, workflow orchestration\, and “frugal twins”. He will also describe the training efforts within the Acceleration Consortium\, intended to reduce the barrier-to-entry for and de-risk the adoption of self-driving labs through a range of online microcourses and in-person training experiences. \n\n\n\nDr. Austin Mroz – Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Consideration \n\n\n\nMulti fidelity Bayesian optimization (MFBO) leverages experimental and or computational data of varying quality and resource cost to optimize towards desired maxima cost effectively. This approach is particularly attractive for chemical discovery due to its ability to integrate multiple information streams. Here\, we investigate the application of MFBO to accelerate the identification of promising molecules and materials. We specifically explore the conditions under which lower fidelity data can enhance performance compared to single-fidelity problem formulations. We then discuss the utility and accessibility of this powerful optimisation framework and introduce a web application to guide users through implementing MFBO in their workflows. \n\n\n\nFollowing the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\n Sterling BairdDirector\, Training and Programs\n\n\n\n\n\nAustin Mroz Postdoctoral Fellow\n\n\n\n\n\nKeith Butler – Webinar Chair Associate Professor in Computational Materials Chemistry
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-january-2025/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241211T140000
DTEND;TZID=UTC:20241211T150000
DTSTAMP:20260412T184014
CREATED:20241122T144151Z
LAST-MODIFIED:20251113T153531Z
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SUMMARY:AIchemy’s Monthly Webinar Series – December 2024
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE11 December\, 2024 TIME14:00 – 15:00 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nMachine Learning hits the Lab: Experiment Planning with Bayesian (Co-)Pilots  Analysing Small Angle X-Ray Scattering Data with Neural Network Accelerated Monte Carlo Sampling \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. Felix Strieth-Kalthoff – Machine Learning hits the Lab: Experiment Planning with Bayesian (Co-)PilotsProf. Felix’s talk will discuss his recent efforts to integrate Bayesian ML tools into experimental laboratory workflows with a focus on data limitations by enhancing ML with expert knowledge to improve decision making. Using examples from synthetic chemistry and conjugated organic materials discovery. He will highlight the opportunities and challenges in ML to support lab-based decisions.   Kelvin Wong – Analysing Small Angle X-Ray Scattering Data with Neural Network Accelerated Monte Carlo SamplingKelvin will present a method for analysing Small Angle X-Ray Scattering (SAXS) curves using Markov Chain Monte Carlo (MCMC) sampling combined with an artificial neural network (ANN) surrogate model. The method reduces the sampling and analysis time\, paving the way for real-time feedback and application in autonomous\, closed-loop laboratories. Following the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nFelix Strieth-KalthoffProfessor of Digital Chemistry\n\n\n\n\n\nKelvin WongDoctoral Researcher\n\n\n\n\n\nJohn Ward – Webinar Chair Senior Lecturer
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-dec/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241120T130000
DTEND;TZID=UTC:20241120T140000
DTSTAMP:20260412T184014
CREATED:20241104T150336Z
LAST-MODIFIED:20251113T153540Z
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SUMMARY:AIchemy’s Monthly Webinar Series - November 2024
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE20 November\, 2024 TIME13:00 – 14:00 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nSelf-Optimising Approaches for Flow Synthesis HypBO: Accelerating Black-Box Scientific Experiments Using Experts’ Hypotheses \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to the next session of our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nDr. Adam Clayton – Self-Optimising Approaches for Flow Synthesis \n\n\n\nDr Adam Clayton’s talk will cover how machine learning and adaptive algorithms like the Adaptive Latent Bayesian Optimiser (AlaBO) can streamline the optimisation of complex\, multistep chemical reactions\, improving efficiency in flow chemistry. He will introduce a new (AlaBO) algorithm\, designed to enhance the development of mixed variable catalytic reactions. \n\n\n\nAbdoulatif Cisse – HypBO: Accelerating Black-Box Scientific Experiments Using Experts’ Hypotheses \n\n\n\nAbdoulatif Cisse’s talk will explore how expert human hypotheses can be integrated with Bayesian optimisation to quickly navigate large\, unexplored scientific search spaces\, particularly in fields like materials discovery. Learn how this method improves the efficiency of search processes\, achieving faster and more accurate results in complex experiments. \n\n\n\nFollowing the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nDr. Adam Clayton Associate Professor\n\n\n\n\n\nAbdoulatif Cisse Research Postgraduate\n\n\n\n\n\nTahereh Nematiaram –  Webinar Chair Strathclyde Chancellor’s Fellow
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-nov/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241023T140000
DTEND;TZID=UTC:20241023T150000
DTSTAMP:20260412T184014
CREATED:20240926T104747Z
LAST-MODIFIED:20250929T102514Z
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SUMMARY:AIchemy's Inaugural Monthly Webinar
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE23 October\, 2024 TIME14:00 – 15:00 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nAutomation and Flow: Opportunities for Supramolecular Chemists Streamlining the Discovery of Porous Organic Cages \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to the first talks in the AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks:Prof. Anna Slater – University of LiverpoolTalk Title: Automation and Flow: Opportunities for Supramolecular Chemists  \n\n\n\nProfessor Anna Slater will explore how continuous flow chemistry and automation can drive advancements in supramolecular chemistry and materials science. The talk will demonstrate how expertise in organic chemistry and non-covalent interactions can be used to fine-tune self-assembled materials for cutting-edge applications. Ideal for flow chemists\, materials scientists\, and anyone interested in the future of material discovery. \n\n\n\nAnnabel Basford – Imperial College LondonTalk Title: Streamlining the discovery of porous organic cages \n\n\n\nAnnabel Basford will present a streamlined hybrid workflow that combines low-cost automated high-throughput experimentation\, automated data analysis for turbidity using computer vision\, ¹H NMR spectroscopy for conversion\, and mass spectrometry for topology identification\, complemented with high-throughput computational modelling of cage structures and to predict shape-persistence. This is combined into a cage database analysis tool – cagey – to accelerate the discovery process of one particular subclass of molecular organic materials\, porous organic cages. This work highlights the advantages of combining these approaches for large-scale data curation towards an accessible data-driven materials discovery approach.Following the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Anna Slater Professor of Chemistry\n\n\n\n\n\nAnnabel Basford Research Postgraduate\n\n\n\n\n\nBecky Greenaway – Webinar Chair Senior Lecturer
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250905T120000
DTEND;TZID=UTC:20250905T120000
DTSTAMP:20260412T184014
CREATED:20240809T084959Z
LAST-MODIFIED:20251113T153552Z
UID:1537-1757073600-1757073600@aichemy.ac.uk
SUMMARY:AIchemy Digital Launch Event
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE5 September\, 2024 TIME11:00 – 12:30 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nIntroduction to AIchemy Hub Forerunner Projects AIchemy Hub Activities ECR Committee Funding Opportunities Industry Engagement \n\n\n\n\n\n\n\n\nJoin us for the digital launch of AIchemy (AI for Chemistry Hub) via MS Teams Webinar.Hear from our Hub’s co-Directors\, Prof Kim Jelfs (Imperial College) and Prof Andy Cooper (University of Liverpool)\, along with other members of the Leadership Team.During this event\, you will: \n\n\n\n\nGain insights into AIchemy’s vision and objectives\n\n\n\nDiscover our research priorities\n\n\n\nHear about the Hub’s activities and events\n\n\n\nFind out about funding opportunities\n\n\n\n\nYou will also have the opportunity to contribute your suggestions on various aspects of the Hub\, helping shape our future programme of activities. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nKim Jelfs AIchemy Hub Co-Director\n\n\n\nChris Mellor AIchemy Hub Co-Manager\n\n\n\n\n\nAndy Cooper AIchemy Hub Co-Director\n\n\n\nBen Alston  AIchemy Hub Co-Manager\n\n\n\n\n\nGraeme Day  Forerunner Project 2 Lead\n\n\n\n\n\nJacqui Coles  Forerunner Project 3 Lead
URL:https://aichemy.ac.uk/event/aichemy-digital-launch-event/
CATEGORIES:Webinar
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