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DTSTART;TZID=UTC:20251210T140000
DTEND;TZID=UTC:20251210T150000
DTSTAMP:20260501T201249
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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BEGIN:VEVENT
DTSTART;TZID=UTC:20260121T140000
DTEND;TZID=UTC:20260121T150000
DTSTAMP:20260501T201249
CREATED:20251125T150202Z
LAST-MODIFIED:20260128T132906Z
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SUMMARY:AIchemy’s Monthly Webinar Series - January 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE21 January 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\nREGISTRATION CLOSED\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\nAndrea Dimitracopoulos – COO & Co-founder\, Deepmirror \n\n\n\nTalk Title: Tackling Drug Design Challenges with Molecular Assay FingerprintsIn the journey from idea to clinic\, drug discovery teams must balance potency with a wide set of ADMET constraints (solubility\, permeability\, CNS penetration\, clearance\, toxicity risk\, and more). AI can reduce the cost and time of small-molecule drug design by improving both property prediction and molecule prioritisation across the design–make–test–analyse (DMTA) cycle. In this talk\, we introduce molecular assay fingerprints as a learned representation for modelling assay endpoints. This approach combines pre-training on a large assay database with programme-specific fine-tuning to improve prediction accuracy. It also leverages Gaussian processes to quantify predictive uncertainty\, which can then be used to prioritise compounds and explore chemical space efficiently – accelerating programme progression. Benchmarking on private drug programmes illustrates how assay fingerprints can improve predictive accuracy for both potency and key ADMET properties\, dramatically reducing the number of design cycles. \n\n\n\nFederico Ottomano – Imperial College LondonTalk Title: Scalable molecular elucidation from IR and NMR spectroscopy using Machine Learning \n\n\n\n\n\nSpeakers\n\n\n\n\n\nAndrea DimitracopoulosCOO & Co-founder\, Deepmirror\n\n\n\n\n\nFederico Ottomano Postdoctoral Research Assistant\, Imperial College London\n\n\n\n\n\nGabriella Pizzuto – Webinar ChairLecturer (Assistant Professor)
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-january-2026/
CATEGORIES:Webinar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260218T140000
DTEND;TZID=UTC:20260218T150000
DTSTAMP:20260501T201249
CREATED:20260128T132849Z
LAST-MODIFIED:20260325T134403Z
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SUMMARY:AIchemy’s Monthly Webinar Series - February 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE18 February 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\nOrganometallic Chemistry x Data Science Rethinking Generative AI for Materials Discovery \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. Natalie Fey – University of Bristol \n\n\n\nTalk Title: Organometallic Chemistry x Data ScienceComputational studies of homogeneous catalysis play an increasingly important role in furthering (and changing) our understanding of catalytic cycles and can help to guide the discovery and evaluation of new organometallic catalysts. While a truly “rational design” process often remains out of reach\, detailed mechanistic information from both experiment and computation can be combined successfully with suitable parameters characterising catalysts and substrates to predict outcomes and guide screening. \n\n\n\nIn this presentation\, I will use examples drawn from our recent work\, including the exploration of maps of chemical space and of a reactivity database\, to illustrate how we are increasingly applying data science techniques for visualisation and prediction\, with the goal of informing the discovery and design of suitable organometallic catalysts. \n\n\n\nHyunsoo Park – Imperial College LondonTalk Title: Rethinking Generative AI for Materials Discovery \n\n\n\nGenerative artificial intelligence (AI) has emerged as a potent paradigm for inverse materials design\, offering the potential to invert traditional discovery workflows by directly proposing structures that satisfy desired properties. However\, a fundamental challenge persists regarding the standard training objectives used in generative AI versus the goals of materials discovery. A critical misalignment exists between the likelihood-based sampling typical of generative modelling and the targeted focus on underexplored regions required to identify novel compounds. \n\n\n\nTo address this challenge\, this talk presents Chemeleon2\, a framework that reformulates crystal generation as a reinforcement learning (RL) task. Through the integration of Group Relative Policy Optimization (GRPO) with latent diffusion models\, the system optimizes multi-objective rewards to simultaneously achieve stability\, diversity\, and novelty. The presentation further details how this methodology facilitates property-guided design\, ensuring chemical validity while isolating desired functionalities. Ultimately\, this approach establishes a modular foundation for controllable\, AI-driven inverse design\, effectively addressing the novelty-validity trade-off inherent in scientific discovery applications. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Natalie FeyProfessor of Chemistry\n\n\n\n\n\nHyunsoo Park Research Associate in Materials Informatics\n\n\n\n\n\nDr. Adam ClaytonWebinar HostAssociate Professor
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-february-2026/
CATEGORIES:Webinar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260311T000000
DTEND;TZID=UTC:20260415T235959
DTSTAMP:20260501T201249
CREATED:20260209T105627Z
LAST-MODIFIED:20260209T110744Z
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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\nWEBINAR 1 Register HERE\n\n\n\nWEBINAR 2 Register HERE\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
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260318T140000
DTEND;TZID=UTC:20260318T150000
DTSTAMP:20260501T201249
CREATED:20260209T224922Z
LAST-MODIFIED:20260325T144026Z
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SUMMARY:AIchemy’s Monthly Webinar Series – March 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE18 March 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\nResolving the data ambiguity for periodic crystals The Crystal Geomap visualises materials databases in real time. \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. Vitaliy Kurlin – University of Liverpool \n\n\n\nTalk Title: Resolving the data ambiguity for periodic crystalsThe discontinuity of cell-based representations of periodic crystals under almost any noise has been known theoretically and experimentally at least since 1965. As a result\, major materials databases accumulated thousands of near-duplicate structures that could not be recognized by any past tools [1]. The latest example is the correction of the A-lab paper in Nature [2]\, where almost all words “novel” and “discovery” were crossed out. We will present a rigorously justified approach to uniquely identifying the atomic structure of any periodic crystal by complete\, continuous and fast geometric codes [3]. \n\n\n\n[1] D.Chawla. C&EN news\, https://cen.acs.org/research-integrity/Duplicate-structures-haunt-crystallography-databases/103/web/2025/12. \n\n\n\n[2] N.Szymanski et al. Author Correction: An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature (2026)\, https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-025-09992-y/MediaObjects/41586_2025_9992_MOESM1_ESM.pdf \n\n\n\n[3] D.Widdowson\, V.Kurlin. Resolving the data ambiguity for periodic crystals. NeurIPS 2022\, v.35\, p.24625-2463. Extended version to appear in SIAM J Appl. Math. 2026\, https://arxiv.org/abs/2108.04798. \n\n\n\nDr. Daniel Widdowson  – University of Liverpool \n\n\n\nTalk Title: The Crystal Geomap visualises materials databases in real time \n\n\n\nOur rigorously justified invariants of crystals give rise to a continuous space containing all crystals\, where the proximity of two crystals does not depend on a choice of unit cell and motif\, but whether the two structures can be closely matched atom for atom by isometry [4]. This led us to develop software to visualise this space and be an interface to ultra-fast comparisons of crystals enabled by our invariants [5]. In this talk we will explore unusual “features” of crystal databases such as the ICSD made visible by our depictions of crystal space\, examples of nearly identical crystals represented with completely different cells and motifs [6]\, and a live example of detection of all geometric (near-)duplicates in the ICSD\, a calculation which was computationally intractable by existing methods. \n\n\n\n[4] O.Anosova\, V.Kurlin\, M.Senechal. The importance of definitions in crystallography. IUCrJ\, v.11(4)\, p.453-463 (2024). \n\n\n\n[5] D.Widdowson\, V.Kurlin. Continuous invariant-based maps of the Cambridge Structural Database. Crystal Growth & Design\, v.24(13)\, p.5627–5636 (2024). \n\n\n\n[6] D.Widdowson\, V.Kurlin. Geographic-style maps with a local novelty distance help navigate the materials space. Scientific Reports\, v.15\, 27588 (2025). \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Vitaliy KurlinProfessor of Computer Science\n\n\n\n\n\nDr. Daniel Widdowson Senior Software Engineer\n\n\n\n\n\nDr. John Ward – Webinar Chair Lecturer in Chemistry
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-march-2026/
CATEGORIES:Webinar
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