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BEGIN:VEVENT
DTSTART;TZID=UTC:20251022T150000
DTEND;TZID=UTC:20251022T160000
DTSTAMP:20250829T132253Z
CREATED:20250829T132253Z
LAST-MODIFIED:20250829T132253Z
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SUMMARY:AIchemy’s Monthly Webinar Series - Oct 25
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE22 October 2025 TIME15:00 – 16:00 COSTFree \n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nEnergy and forces are all you need Device-scale simulations of memory materials enabled by fast and accurate interatomic potentials \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:Prof. Bingqing Cheng – UC BerkeleyTalk Title: Energy and forces are all you need \n\n\n\nStandard machine learning interatomic potentials (MLIPs) often rely on short-range approximations\, limiting their applicability to systems with significant electrostatics. We recently introduced the Latent Ewald Summation (LES) method\, which learns long-range electrostatics from *just energy and force data*. We show that LES can effectively infer physical partial charges\, polarization and Born effective charge (BEC) tensors\, as well as achieve better accuracy compared to methods that explicitly learn charges. As demonstrations\, we predict the infrared spectra of bulk water under zero or finite external electric fields\, ionic conductivities of high-pressure superionic ice\, and the phase transition and hysteresis in ferroelectric PbTiO3 perovskite.  \n\n\n\nYuxing Zhou – University of OxfordTalk Title: Device-scale simulations of memory materials enabled by fast and accurate interatomic potentials \n\n\n\nAtomistic simulations play an important role in understand fundamental properties and working mechanisms of phase-change materials (PCM)-based devices. Our recent work has shown that machine-learning (ML)-driven molecular dynamics simulations enable accurate description of Ge–Sb–Te alloys\, particularly for compounds on the GeTe–Sb2Te3 tie-line (GST)1. Using an ML potential based on the Gaussian approximation potential (GAP) framework\, we demonstrate a device-scale RESET (“1→0”) simulation over 50 ps in a device-scale model of 532\,980 atoms (corresponding to a real device size of 40 × 20 × 20 nm3; Fig. 1). However\, realistic switching operations in GST devices usually take tens of nanoseconds. More importantly\, non-isothermal conditions are prominent in GST devices\, which can lead to distinct SET or RESET states as compared to isothermal conditions\, thus complicating accurate modelling of phase transitions in real devices.In this talk\, I will demonstrate full-cycle device-scale simulations of GST devices under realistic programming conditions. I will introduce a new ML potential based on the Atomic Cluster Expansion (ACE) framework2. The new ACE potential is more than 400 times faster than the GAP potential\, which enables full-loop simulations (multiple RESET to SET operations) of cross-point and mushroom-type devices at extensive length scales (involving sub-million atoms) and time scales (tens of nanoseconds). Next\, I will present a new simulation protocol that describes non-isothermal conditions and temperature gradients of any desired level of spatiotemporal complexity. Based on these ML-driven MD simulations\, we show temperature-dependent crystallisation behaviours of GST\, elucidating the interplay between nucleation and growth under non-isothermal crystallisation in GST memory devices. This talk presents a platform for the predictive modelling of PCM-based memory devices\, and more widely\, it highlights the power of highly scalable atomistic machine-learning models for modern materials science and engineering.Following the presentations\, there will be time for questions from the audienceWe are delighted to welcome you to the first talks in the AIchemy Hub’s monthly webinar series. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nProf. Bingqing Cheng Professor of Chemistry\n\n\n\n\n\nYuxing Zhou Schmidt AI in Science Fellow at Oxford Chemistry\n\n\n\n\n\nDr. Adam ClaytonWebinar Host – Associate Professor
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-oct-25-2/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20251119T140000
DTEND;TZID=UTC:20251119T150000
DTSTAMP:20250829T151653Z
CREATED:20250829T151653Z
LAST-MODIFIED:20250829T151653Z
UID:12272-1763560800-1763564400@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series - November 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE19 November 2025 TIME14:00 – 15:00 COSTFree \n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nBridging Molecular Design and Synthesis: AI and Automation for Reaction Optimisation Screening first\, AI on top \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. Lauren Ye Seol Lee – UCL \n\n\n\nTalk Title: Bridging Molecular Design and Synthesis: AI and Automation for Reaction Optimisation \n\n\n\nAchieving efficient and sustainable chemical synthesis requires bridging molecular-level understanding with data-driven process optimisation. This seminar presents an integrated framework that connects molecular representation\, reaction modelling\, and autonomous experimentation\, illustrated through applications in pharmaceutical manufacturing. \n\n\n\nWe first enhance reaction yield prediction by embedding physically meaningful molecular descriptors and reaction context\, enabling models to capture both intra- and intermolecular interactions. We then extend this framework to model solvation effects on reaction kinetics\, where explicitly incorporating transition-state structures provides mechanistic insight into solvent-mediated reactivity. In parallel\, we model single-component solubility with explicit temperature dependence\, by establishing a foundation for future mixture and formulation predictions relevant to pharmaceutical processes. Finally\, we highlight ongoing work on automated amide synthesis using robotic platforms\, aiming to close the loop between computation\, experimentation\, and sustainable process development. \n\n\n\n\n\n\n\nDr. Omer Omar – CEO\, Apoello \n\n\n\nTalk Title: Screening first\, AI on top. \n\n\n\nHigh-throughput virtual screening is a method gaining quick traction in the world of materials discovery\, particularly for organic electronics. It finds its origins in drug discovery where simple cheminformatics can reign supreme; however\, to build vast datasets of optoelectronic properties required the advent of advanced hardware and (quantum chemical) code. The method is a simple funnel — look at exhaustive sets and filter down to the most promising candidates based on more and more stringent criteria. This is often done with de novo material libraries for given scaffolds for a given property. We go further by asking: what if we screened the whole synthetically-available chemical space? This way\, you can screen for multiple properties across the breadth of chemistry that exists today and determine the limit of exciting discoveries. This talk will go into the discoveries made through screening both with theory only and combined theoretical-experimental approaches. We will also consider: once we have the data\, how can ML & AI be used for i) improving the discovery engine\, and ii) bringing digestible insights to researchers who want to instantly discover new materials? \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. Lauren Ye Seol LeeAssistant Professor – UCL\n\n\n\n\n\nOmer Omar CEO – Apoello\n\n\n\n\n\nTahereh Nematiaram Webinar Host
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-november-2025-2/
CATEGORIES:Webinar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20251210T140000
DTEND;TZID=UTC:20251210T150000
DTSTAMP:20251125T132616Z
CREATED:20251125T132616Z
LAST-MODIFIED:20251125T132616Z
UID:12292-1765375200-1765378800@aichemy.ac.uk
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-2025-2/
CATEGORIES:Webinar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260121T140000
DTEND;TZID=UTC:20260121T150000
DTSTAMP:20251125T150202Z
CREATED:20251125T150202Z
LAST-MODIFIED:20251125T150202Z
UID:12296-1769004000-1769007600@aichemy.ac.uk
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-2026-2/
CATEGORIES:Webinar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260218T140000
DTEND;TZID=UTC:20260218T150000
DTSTAMP:20260128T132849Z
CREATED:20260128T132849Z
LAST-MODIFIED:20260128T132849Z
UID:12212-1771423200-1771426800@aichemy.ac.uk
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-2026-2/
CATEGORIES:Webinar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20260311T000000
DTEND;TZID=UTC:20260415T235959
DTSTAMP:20260209T105627Z
CREATED:20260209T105627Z
LAST-MODIFIED:20260209T105627Z
UID:12224-1773187200-1776297599@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-2/
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:20260318T140000
DTEND;TZID=UTC:20260318T150000
DTSTAMP:20260325T144026Z
CREATED:20260209T224922Z
LAST-MODIFIED:20260325T144026Z
UID:7477-1773842400-1773846000@aichemy.ac.uk
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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END:VEVENT
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
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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
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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
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