BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//aichemy - ECPv6.17.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:aichemy
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:20260422T140000
DTEND;TZID=UTC:20260422T150000
DTSTAMP:20260818T110355Z
CREATED:20260325T142551Z
LAST-MODIFIED:20260818T110355Z
UID:12248-1776866400-1776870000@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\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-april-2026-2/
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:20260520T140000
DTEND;TZID=UTC:20260520T150000
DTSTAMP:20260818T112222Z
CREATED:20260508T151957Z
LAST-MODIFIED:20260818T112222Z
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\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\n FLIP: Flowability-Informed Powder Weighing \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 \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-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:20260617T140000
DTEND;TZID=UTC:20260617T150000
DTSTAMP:20260818T110613Z
CREATED:20260601T105940Z
LAST-MODIFIED:20260818T110613Z
UID:12196-1781704800-1781708400@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\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nCombinatorial explosion: from atom-bond arrangements to exotic diseases Low-cost mechanism-informed features enable transferable enantioselectivity predictions from sparse data \n\n\n\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. Timothy 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\nDr. Simone Gallarati – University of Utah \n\n\n\nTalk Title: Low-cost mechanism-informed features enable transferable enantioselectivity predictions from sparse data \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\nDr.Simone 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-2026-2/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/06/June-Webinar-26-1.png
END:VEVENT
END:VCALENDAR