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BEGIN:VEVENT
DTSTART;TZID=UTC:20260318T140000
DTEND;TZID=UTC:20260318T150000
DTSTAMP:20260430T132651
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260311T000000
DTEND;TZID=UTC:20260415T235959
DTSTAMP:20260430T132651
CREATED:20260209T105627Z
LAST-MODIFIED:20260209T110744Z
UID:7430-1773187200-1776297599@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\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
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/02/IP-Webinars.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260218T140000
DTEND;TZID=UTC:20260218T150000
DTSTAMP:20260430T132651
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
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2026/01/AIchemy-Webinar-Feb-26.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260121T140000
DTEND;TZID=UTC:20260121T150000
DTSTAMP:20260430T132651
CREATED:20251125T150202Z
LAST-MODIFIED:20260128T132906Z
UID:6344-1769004000-1769007600@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/
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://aichemy.ac.uk/wp-content/uploads/2025/11/Jan-26-Webinar-ft-image.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20251210T140000
DTEND;TZID=UTC:20251210T150000
DTSTAMP:20260430T132651
CREATED:20251125T132616Z
LAST-MODIFIED:20260325T135926Z
UID:6325-1765375200-1765378800@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/
CATEGORIES:Webinar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20251119T140000
DTEND;TZID=UTC:20251119T150000
DTSTAMP:20260430T132651
CREATED:20250829T151653Z
LAST-MODIFIED:20260325T144038Z
UID:4970-1763560800-1763564400@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/
CATEGORIES:Webinar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20251022T150000
DTEND;TZID=UTC:20251022T160000
DTSTAMP:20260430T132651
CREATED:20250829T132253Z
LAST-MODIFIED:20260209T113256Z
UID:4956-1761145200-1761148800@aichemy.ac.uk
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/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250917T140000
DTEND;TZID=UTC:20250917T150000
DTSTAMP:20260430T132651
CREATED:20250730T142651Z
LAST-MODIFIED:20260209T113357Z
UID:4437-1758117600-1758121200@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series - Sept 25
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE17 September 2025 TIME14:00 – 15:00 COSTFree \n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nThe bittersweet lesson of scaling in AI for materials Accelerated Porosity Screening Using a Multichannel Colorimetric Array \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. Rafael Gomez-Bombarelli\, MIT Department of Materials Science and Engineering   \n\n\n\nTalk Title: The bittersweet lesson of scaling in AI for materials \n\n\n\nAI has the potential to bring much-needed acceleration to the development of chemicals and materials for energy and sustainability\, just like it has delivered intelligence gains in other fields. The path to success goes through scaling\, perhaps exclusively through scaling. Rich Sutton’s ‘bitter lesson’ states that “general methods that leverage computation are ultimately the most effective” in AI. The corollaries are bittersweet in chemistry and materials.  \n\n\n\nThe lesson permeates through the use of AI in chemistry and materials. The tremendous success of universal ML interatomic potentials (MLIPs) as surrogate for quantum-mechanical energies and forces is unarguable. Trained on 100-million-count datasets\, they reflect scaling laws similar to the ones in language or vision models\, generalizing to new scientific questions and enabling simulations that were intractable a few years ago. Generative models\, trained mostly on the same synthetic data\, are currently being used to propose novel materials at machine speed. The discussion around the power of inductive bias (energy conservation\, equivariance) and whether it is better reflected in model architecture or in training strategy is very much ongoing. The physical sciences may well be the last holdout for domain knowledge and inductive bias\, or maybe they will ultimately follow the same trends as other domains.  \n\n\n\nBut MLIPs are surrogates for physics-based simulators\, and arguably\, materials only truly matter if they are made in the lab and then scaled up industrially and commercially. This is the truly bitter scaling lesson in materials. The promise of AI for chemistry and materials needs to be realized through very traditional\, expensive and slow channels. In this talk\, I will describe our group’s work both in the highly scalable fusion of simulations and machine learning\, and in the “high-contact” and lower throughput effort of translating AI designs into tangible\, scalable products\, in areas like heterogeneous catalysis\, battery materials or sustainable polymers \n\n\n\nYushu Han – University of Liverpool \n\n\n\nTalk Title: Accelerated Porosity Screening Using a Multichannel Colorimetric Array  \n\n\n\nPorous materials are important for many technologies\, but the measurement of porosity by gas adsorption isotherms is slow\, taking around one day per sample using a single-port gas sorption analyser\, even when using a ‘quick’ analysis method with relatively few data points. With the increased use of automated platforms for material generation\, porosity analysis is now frequently the bottleneck in the discovery of new porous materials. Here\, we present a semi-automated pre-screening strategy that uses dye adsorption to create a colorimetric array that is combined with computer vision analysis for porosity screening. By using a six-dye multichannel array and a defined porosity threshold\, our method rapidly screened 50 candidate materials that spanned molecular solids\, polymers\, and metal-organic frameworks. The method showed a 98–100% classification accuracy compared with gas uptake measurements. While this method is more qualitative than quantitative\, it is more than 30 times faster than conventional gas sorption measurements\, and it has the scope to be made much faster with greater parallelization and automation. This makes this colorimetric method suitable for pre-screening arrays of materials to choose samples that merit more detailed conventional porosity analysis. \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\nProf. Rafael Gomez-Bombarelli Professor of Chemistry\n\n\n\n\n\nYushu HanPhD student\n\n\n\n\n\nAdam ClaytonWebinar Host – Associate Professor
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-sept-25/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250723T140000
DTEND;TZID=UTC:20250723T150000
DTSTAMP:20260430T132651
CREATED:20250409T091018Z
LAST-MODIFIED:20250923T085516Z
UID:3285-1753279200-1753282800@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – July 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE23 July 2025 TIME14:00 – 15:00 COSTFree \n\n\n\nREGISTration closed\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\nProfessor Jason Hein – Real-Time Data and Modular Robotics for Scalable Workflow Automation The future of chemistry demands experimental systems that are flexible\, data rich\, and built for iteration. This talk will explore how modular automation platforms that integrate real time analytics with robotic execution are transforming how we develop\, optimize\, and scale chemical processes. I will share our experience building reconfigurable systems that connect high resolution analytical tools with programmable robotics to drive autonomous workflows across applications from crystallization to liquid-liquid extraction. Two key examples will be highlighted: DirectInjection\, a real time HPLC-MS integration system that enables online monitoring and control in complex reaction environments; and IvoryOS\, an open source orchestration framework designed to coordinate modular hardware across diverse lab tasks. Together\, these tools support scalable\, chemist-in-the-loop workflows that balance autonomy with insight and help accelerate discovery while preserving the nuance of expert-driven chemistry. \n\n\n\nSriram Vijayakrishnan – Autonomous Mobile Robots For Exploratory Synthetic Chemistry \n\n\n\nAutonomous laboratories can accelerate discoveries in chemical synthesis\, but this requires automated measurements coupled with reliable decision-making Most autonomous laboratories involve bespoke automated equipment  and reaction outcomes are often assessed using a single\, hard-wired characterization technique. Any decision-making algorithms must then operate using this narrow range of characterization data. By contrast\, manual experiments tend to draw on a wider range of instruments to characterize reaction products\, and decisions are rarely taken based on one measurement alone. Our modular workflow combines mobile robots\, an automated synthesis platform\, a liquid chromatography–mass spectrometer and a benchtop nuclear magnetic resonance spectrometer. A heuristic decision-maker processes the orthogonal measurement data\, selecting successful reactions to take forward and automatically checking the reproducibility of any screening hits. This strategy is particularly suited to exploratory chemistry that can yield multiple potential products\, as for supramolecular assemblies. \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\nJason Hein Professor of Chemistry\n\n\n\n\n\nSriram Vijayakrishnan Postdoctoral Researcher\n\n\n\n\n\nGabriella Pizzuto – Webinar Chair Lecturer (Assistant Professor)
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-july-2025/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250618T130000
DTEND;TZID=UTC:20250618T140000
DTSTAMP:20260430T132651
CREATED:20250611T154323Z
LAST-MODIFIED:20250619T151012Z
UID:3630-1750251600-1750255200@aichemy.ac.uk
SUMMARY:AIchemy Frontier Fund Launch Webinar
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE18 June\, 2025 TIME13:00 – 13:30 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube link below to watch the webinar. \n\n\n\n\nFrontier Fund Launch Webinar \n\n\n\n\n\n\n\n\nJoin us for the Launch Webinar of Our New Funding Call \n\n\n\nWe’re excited to invite you to a webinar introducing our latest funding opportunity. This session will provide a comprehensive overview of the call\, including key details on eligibility\, application criteria\, timelines\, and the support available. \n\n\n\nWhether you’re considering applying or simply want to learn more\, this is your opportunity to hear directly from the team\, ask questions\, and get the information you need to get started. \n\n\n\nAll relevant information about the funding call will be covered during the session \n\n\n\n\n\nSpeakers\n\n\n\n\n\nKim Jelfs AIchemy Hub Co-Director\n\n\n\n\n\nAndy Cooper AIchemy Hub Co-Director
URL:https://aichemy.ac.uk/event/aichemy-frontier-fund-launch-webinar/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250521T183000
DTEND;TZID=UTC:20250521T210000
DTSTAMP:20260430T132651
CREATED:20250415T103304Z
LAST-MODIFIED:20250605T152137Z
UID:3316-1747852200-1747861200@aichemy.ac.uk
SUMMARY:Pint of Science @ Liverpool
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE21 May\, 2025 TIME1830 – 21:00 COST£5 \n\n\n\nSOLD OUT\n\n\n\nREAD THE HIGHLIGHTS BLOG\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nFrederiks32 Hope Street\,\nLiverpool L1 9BX \n\n\n\n\n\n\n\n\n\n\n\n\nJoin us for a night where artificial intelligence meets actual intelligence – yours\, after a pint or two. You’ve heard of AI and GPT – now meet the scientists using it to change the world. Discover how AI is revolutionising chemistry\, engineering\, and even the nuclear industry. Come for the cutting-edge science\, stay for the cold pints and curious conversations.
URL:https://aichemy.ac.uk/event/pint-of-science-liverpool/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250521T150000
DTEND;TZID=UTC:20250521T160000
DTSTAMP:20260430T132651
CREATED:20250315T073308Z
LAST-MODIFIED:20250731T134822Z
UID:2963-1747839600-1747843200@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – May 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE21 May\, 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\nApproximate Bayesian Inference for Spectroscopy with Varying Pure Component Signals Development of ML methods for reaction optimization and prediction \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\nJames Odgers – Approximate Bayesian Inference for Spectroscopy with Varying Pure Component Signals \n\n\n\nSpectroscopy is a widely applied technique for monitoring chemical processes and the physics underpinning the technique is well understood. However\, when making quantitative predictions the physical basis for spectroscopy is typically discarded due to complex effects of the environment on the observed spectral signals. In this work we present a non-parametric Bayesian approach to model the effects of changing conditions\, allowing an interpretable\,  physics informed model to be applied in more challenging situations.  \n\n\n\nProf. Abigail Doyle – Development of ML methods for reaction optimization and prediction \n\n\n\nMachine learning (ML)\, the development and study of computer algorithms that learn from data\, is increasingly important across a wide array of applications\, from virtual personal assistants to social media and product recommendation systems. ML methods have also driven key developments in the natural sciences: virtual screening of drug-like molecules for medical applications\, rapid prediction of physical data\, and computer-aided synthesis planning have all been facilitated by ML. The development of ML tools for synthetic methodology development and catalysis could enable chemists to make data-efficient choices and learn from that data in the course of reaction prediction\, reaction condition optimization\, and mechanistic interrogation. This lecture will describe my group’s efforts to develop and apply open-source data science tools to numerous aspects of synthetic methodology development\, including substrate scope design\, ligand discovery\, reaction optimization and mechanistic elucidation. \n\n\n\nFollowing the presentations\, there will be time for questions from the audience.We look forward to having you attend the event! \n\n\n\n\n\nSpeakers\n\n\n\n\n\nJames OdgersPhD Student\n\n\n\n\n\nAbigail DoyleProfessor of Chemistry\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-may-2025/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250423T140000
DTEND;TZID=UTC:20250423T150000
DTSTAMP:20260430T132651
CREATED:20250315T073147Z
LAST-MODIFIED:20250603T161822Z
UID:2958-1745416800-1745420400@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – April 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE23 April\, 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\nEfficient Data Collection for Efficient Decision Making in Process Development  Don’t throw your data away! Gaining meaningful insights from high-throughput chemistry \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\nLinden Schrecker – Efficient Data Collection for Efficient Decision Making in Process Development  \n\n\n\nReaction data is important to collect not only for theoretically understanding chemistry better\, but also for sustainably mass-producing chemicals for medicine\, agriculture\, and society in general. Chemistry has been progressing towards more data driven and modelling-based approaches\, however in many areas the availability of chemical reaction data is a limiting factor. The development of automated transient flow methods provides an efficient tool by which reaction data can be collected to accelerate our understanding of chemical reactions. \n\n\n\nOver the past decade\, transient flow has been primarily applied to the collection of time-series reaction data and investigation of the effect of reaction temperature. In our work\, we have extended the parameter space which can be investigated further studying the effects of time\, temperature\, reactant stoichiometry\, substituent\, and solvent. Versatile application of these efficient labour-saving techniques to kinetics\, selectivity\, and solvent effects have provided not only academic interest\, but also robust methods for improving sustainability in chemical industry. This improved data collection can be utilized to de-risk decision making in process development. \n\n\n\n\n\n\n\nIn this presentation\, transient flow as a technique will be discussed as a tool for efficiently building understanding of chemical reactions. The insights gained from novel transient flow methods into academic and industrial processes ranging from simple homogenous reactions to heterogeneously catalysed reactions will be presented. Optimal automated workflows for collection and utilization of transient flow data will be discussed\, focusing on applications to kinetics\, selectivity\, and solvent replacement for accelerating process development through machine learning models as utilized within SOLVE. SOLVE is a spin-out from Imperial College London focusing on making chemical processes more efficient through autonomous high-throughput reaction data collection to construct AI reaction space models. SOLVE currently serves clients across the agrochemical\, pharmaceutical\, and fine chemical space \n\n\n\nNessa Carson – Don’t throw your data away! Gaining meaningful insights from high-throughput chemistry \n\n\n\nWith the power of automation\, chemists are enabled to run rapid experimentation and concomitantly generate large amounts of valuable data. However\, without generating these data sensibly for reuse\, a proportion of the insights and value that should be inherent to high-throughput experimentation (HTE) is thrown away. Additionally\, chemists nearly always underestimate how often and in how many ways even the most unremarkable automation data will be reused\, if they become accessible.Through software automation and user-friendly integrations\, we can allow HTE to realize its full impact and foster insight-driven research to unlock new opportunities in chemistry. This work is not “magic”\, but requires user-friendly systems that enhance rather than waste the time of the lab users running them.This talk will explore practical strategies for harnessing the valuable information from lab automation to empower scientists to easily access\, interpret\, and reuse the data generated from HTE \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\nLinden SchreckerFounder and CEO\, SOLVE Chemistry\n\n\n\n\n\nNessa CarsonDigital Champion for Pharmaceutical Sciences\, AstraZeneca\n\n\n\n\n\nAlex Ganose – Webinar Chair Senior Lecturer\, Imperial
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-april-2025/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250319T140000
DTEND;TZID=UTC:20250319T150000
DTSTAMP:20260430T132651
CREATED:20250129T152853Z
LAST-MODIFIED:20250603T152338Z
UID:3056-1742392800-1742396400@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – March 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE19 March\, 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\nHigh-throughput robotic chemical titration using computer vision Teaching Language Models to SpeChemistry: From Design to Synthesis \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\nYuan Li – High-throughput robotic chemical titration using computer vision. \n\n\n\nA high-throughput (HTE) robotic titration workstation was developed using a commercial liquid handling robot (Opentrons OT-2) and computer vision-based analysis. While designed for multiple titration applications\, hydrogen peroxide(H2O2) determination serves as the most elaborate and well-characterized demonstration of its capabilities. To monitor the colour change(from colourless to pale pink)\, a webcam was installed on the OT-2 pipette mount\, capturing real-time titration progress. Image analysis was enhanced through VGG-augmented UNet  for segmentation and the CIELab colour model \, ensuring robust and reproducible detection of subtle colour changes. The sensitivity test of the computer vision-aided colour analysis was strongly correlated to UV-Vis spectroscopy (R2 = 0.9996)\, with a good linear dynamic range at low concentrations. The analytical accuracy of this workstation was ± 11.9% in a 95% confidence interval and its corresponding absolute concentration difference was only 0.50 mM. To validate its real-world applicability\, this workstation was first deployed to monitor the photoproduction of H2O2 over a conjugated polymer photocatalyst\, DE7.  In addition to performing redox titrations\, we demonstrated that the workstation can also be used for acid-base titration and complexometric titration\, capturing a diverse range of colour changes too.   \n\n\n\nProf. Philippe Schwaller – Teaching Language Models to Speak Chemistry: From Design to Synthesis \n\n\n\nArtificial Intelligence is transforming how we approach chemical research and synthesis. By teaching language models to understand and generate the language of chemistry\, we have developed complementary AI systems that bridge the gap between computational design and experimental reality. Our large language model system\, ChemCrow\, represents one of the first demonstrations of an AI system directly controlling robotic synthesis platforms\, successfully executing the synthesis of compounds including organocatalysts and chromophores. Complementing this\, our small language model system\, Saturn\, currently the most sample-efficient molecular design algorithm\, enables precise molecular generation with built-in synthesizability constraints. Saturn’s innovations include direct optimization against retrosynthetic predictions and integration of building block availability\, ensuring that generated molecules are practically accessible. Our work demonstrates how different scales of language models can work together to transform chemical research\, from initial molecular design through to physical synthesis\, potentially revolutionizing drug discovery\, catalysis\, and materials development. \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\nYuan LiPhD student\n\n\n\n\n\nPhilippe SchwallerAssistant Professor \n\n\n\n\n\nGabriella Pizzuto – Webinar Chair Lecturer (Assistant Professor)
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-march-2025/
CATEGORIES:Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250219T140000
DTEND;TZID=UTC:20250219T150000
DTSTAMP:20260430T132651
CREATED:20241122T151601Z
LAST-MODIFIED:20250603T133054Z
UID:2329-1739973600-1739977200@aichemy.ac.uk
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:20260430T132651
CREATED:20241122T150721Z
LAST-MODIFIED:20251113T153521Z
UID:2319-1737558000-1737561600@aichemy.ac.uk
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:20260430T132651
CREATED:20241122T144151Z
LAST-MODIFIED:20251113T153531Z
UID:2310-1733925600-1733929200@aichemy.ac.uk
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:20260430T132651
CREATED:20241104T150336Z
LAST-MODIFIED:20251113T153540Z
UID:2128-1732107600-1732111200@aichemy.ac.uk
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:20260430T132651
CREATED:20240926T104747Z
LAST-MODIFIED:20250929T102514Z
UID:1842-1729692000-1729695600@aichemy.ac.uk
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:20260430T132651
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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