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DTSTART;TZID=UTC:20260513T090000
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SUMMARY:Applications of AI for Catalysis and Energy Materials Discovery
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE13th May 2026 TIME09:00 – 18:00 COSTFree to Attend \n\n\n\nbook now\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nMolecular Sciences Research Hub (MSRH)\,  Imperial College London82 Wood Ln\, London W12 0BZ \n\n\n\n\n\n\n\n\nJoin us for a one-day symposium bringing together researchers working in catalysis and energy materials with AI experts developing and applying machine learning methods in this space.  \n\n\n\nThe programme features talks from leading researchers across chemistry\, materials science\, and AI\, showcasing real-world applications of data-driven approaches in catalysis\, interfaces\, electrolytes\, membranes\, and energy materials. \n\n\n\nThe day will combine invited talks\, discussion\, and dedicated networking time\, providing opportunities to share ideas\, learn about emerging methods\, and spark new interdisciplinary collaborations across experimental\, computational and machine learning research in the area of catalysis and energy materials. \n\n\n\nWho Should Attend:\n\n\n\nThis symposium is designed for: \n\n\n\n\nAcademics and researchers in catalysis\, interfaces\, and energy materials who are interested in applying AI and machine learning to enhance\, accelerate\, or expand their research.\n\n\n\nAI and machine learning experts looking to apply novel computational approaches to challenges in catalysis\, reaction engineering\, molecular discovery\, and energy‑relevant materials.\n\n\n\n\nWhether your work is experimental\, theoretical\, computational\, or method‑development‑focused\, this event offers a unique opportunity to build connections across disciplines and gain insights into the rapidly evolving intersection of AI\, catalysis\, and materials discovery. \n\n\n\n\n\n\n\n\nAgenda\n\n\n\nTimeSession09:00 – 09:30Registration and Coffee09:30 – 09:40Welcome & Opening Remarks – Maryam Mansoori Kermani (Imperial)Chair: Carla Molteni (KCL)09:40 – 10:15Speaker: Evgeny A. Pidko (Delft University of Technology)Phase Boundaries under CO₂ Hydrogenation Conditions on Ni-Ga Catalyst: Coincidence? I Think Not!10:15 – 10:50Speaker: Fernanda Duarte (University of Oxford)Reaction Modelling in the Age of Machine Learning10:50 – 11:20Coffee BreakChair: Chun Ann Huang (Imperial)11:20 – 11:55Speaker: Ricardo Grau Crespo (Queen Mary University of London)Designing materials for thermoelectric applications: density functional theory and machine learning11:55 – 12:30Speaker: James Durrant (University of Oxford) Optical Spectroscopic Analysis of Metal Oxide: Electrolyte Interfaces for Photo- and Electro-Catalysis12:30 – 13:30Lunch BreakChair: Shijing Sun (Cambridge)13:30 – 14:05Speaker: James A. Dawson (Newcastle University) Interfacial Design in Next-Generation Solid Electrolytes14:05 – 14:40Speaker: Qilei Song (Imperial) Molecular Engineering of Polymer Membranes for Sustainable Separation and Energy Processes14:40 – 15:10Coffee Break                      Chair: Sam Cooper (Imperial)15:10 – 15:45Speaker: Jacqueline Cole (University of Cambridge)Data-driven Materials Science for Energy-Sustainable Applications15:45 – 16:20Speaker: Maria Lukatskaya (ETH Zürich)Engineering Local Chemical Environments in Electrolytes for Efficient Batteries16:20 – 16:30Closing remark by Jarvist Frost (Imperial)16:30Networking\n\n\n\n\n\nSpeakers:\n\n\n\n\nProf. Evgeny Pidko – Delft University of Technology (TU Delft)\n\n\n\nProf. Fernanda Duarte – University of Oxford\, Department of Chemistry\n\n\n\nProf. Jacqui Cole – University of Cambridge\n\n\n\nDr. James Dawson – Newcastle University\n\n\n\nProf. James Durrant – University of Oxford\n\n\n\nProf. Maria Lukatskaya – ETH Zürich\n\n\n\nDr. Ricardo Grau‑Crespo – Queen Mary University of London\n\n\n\nDr. Qilei Song – Imperial College London\n\n\n\n\n\n\nTalk Abstracts:\n\n\n\nDesigning materials for thermoelectric applications: density functional theory and machine learning \n\n\n\nDesigning high-performance thermoelectric materials requires optimising a delicate balance of electronic transport\, lattice thermal conductivity\, and chemical stability. This talk will introduce a combined density functional theory (DFT) and machine-learning (ML) framework for accelerating this search. I will first outline how advanced first-principles workflows\, including anharmonic force-constant modelling and Boltzmann transport calculations\, provide detailed insight into carrier transport and heat flow in chalcopyrite compounds and related materials. I will then show how ML-based models such as hiPhive and CraTENet can dramatically reduce computational cost\, enabling rapid prediction of electronic and thermal transport properties across very large chemical spaces. Finally\, I will discuss how composition-driven screening coupled with generative AI crystal structure prediction techniques might open new routes for discovering Earth-abundant\, high-zT thermoelectrics. \n\n\n\nOptical Spectroscopic Analysis of Metal Oxide: Electrolyte Interfaces for Photo- and Electro-Catalysis  \n\n\n\nI will start by discussing the potential importance of both photoelectrocatalytic and electrocatalytic strategies in our transition to more sustainable energy systems. I will consider both the importance of water splitting to yield molecular hydrogen\, and also the wider challenges of CO2 and nitrogen reduction. A key challenge is eluciation of the materials design requirements for efficient catalytic function. I will go on to discuss the use of optical spectroscopies to provide insight into (photo)electrocatalytic function\, employing water oxidation as our most widely studied exemplar. In particular I will discuss how operando optical spectroelectrochemistry can used to determine redox state population densities and kinetics in metal oxides electrodes and photoelectrodes during water oxidation. This spectroelectrochemical approach is based on the idea that the redox states of most transition metal oxides are coloured\, allowing the specific concentrations of each state to be tracked by their optical absorption/reflection as a function of material\, applied bias\, time\, electrolyte etc. Such optical data can be complimentary to more widely employed electrochemical analyses such as J/V plots and Tafel analyses. In my talk I will discuss examples of the insights gained from operando spectroelectrochemistry into the design and function of a selection of materials\, including Iridium Oxide and Nickel/Iron Oxyhydroxide electrocatalysts\, as well as Hematite\, Bismuth vanadate and Strontium Titanate photoelectrodes/photocatalysts. \n\n\n\nInterfacial Design in Next-Generation Solid Electrolytes \n\n\n\nInterfaces are central to the performance\, functionality and longevity of energy materials and devices\, whether they be batteries\, fuel cells or a plethora of other crucial technologies. In solid-state batteries\, interfaces govern how efficiently ions move between the solid electrolyte and the electrodes. Poorly engineered interfaces can lead to high resistance\, reduced performance and even instability\, while well-designed interfaces enhance conductivity\, battery life and safety. In the first part of this talk\, I will discuss how machine learning interatomic potentials have enabled us to explore interfaces and ion transport at scales far beyond the capabilities of ab initio molecular dynamics. This includes the investigation of solid electrolyte interphase formation between argyrodite Li6PS5Cl and Li metal and universal cation transport in LaCl and LaBr-based solid electrolytes. In the second part of this talk\, I will present our recent atomistic simulations of grain boundaries in topical solid electrolytes\, including garnet Li7La3Zr2O12 and argyrodite Li6PS5Cl. Specifically\, I will discuss how the different electronic structures of Li7La3Zr2O12 and Li6PS5Cl grain boundaries lead to fundamentally different mechanisms of lithium ion incorporation and reduction and lithium atom nucleation in these two materials. \n\n\n\nData-driven Materials Science for Energy-Sustainable Applications \n\n\n\nThis lecture presents the development and application of data-extraction tools andmachine-learning (ML) models that are enabling data-driven materials science forenergy-sustainable applications. The talk describes how to source high-quality data tobuild custom databases and apply them to (a) train ML models; (b) realise data-drivenmaterials discovery; (c) conduct data-driven materials science for the energy sector; (f)create energy-efficient domain-specific language models for materials science to feedagentic AI and help democratise AI for the materials research community. The casestudies presented track a theme of energy-sustainable materials \n\n\n\nEngineering Local Chemical Environments in Electrolytes for Efficient Batteries \n\n\n\n“Electrolytes play a crucial role in energy storage devices\, impacting their environmental footprint\, safety\, cost\, and performance. This talk will cover two areas of electrolyte research for improving batteries. \n\n\n\nFirst\, we will explore water-based batteries. Aqueous electrolytes\, being non-flammable and less toxic\, offer safer battery operation. However\, their limited electrochemical stability window reduces energy density. To address this\, highly concentrated “”water-in-salt”” (WIS) electrolytes have been developed\, significantly expanding the stability window and enhancing the performance of Li-ion and Zn metal batteries for grid energy storage. Despite their advantages\, WIS electrolytes have high viscosity and require large amounts of potentially toxic salts\, which limits their usability. We will discuss how cation solvation\, electrolyte structure\, and hydrogen bonding influence the electrochemical properties and performance\, particularly in Zn plating/stripping and electrolyte decomposition. Additionally\, we will explore strategies for engineering relatively dilute electrolytes for efficient aqueous batteries. \n\n\n\nSecond\, the talk will present a novel method for stabilizing interfaces in Li metal batteries (LiMBs). Conventional electrolytes result in low cycle life and safety issues due to “”dead”” lithium and dendrite formation. Prior research suggests that fluorine-rich interfacial layer chemistry is important for the stabilization of Li-metal anodes\, which can be achieved when electrolytes with a high fraction of fluorinated solvents and/or salts are used. We propose an alternative approach using electrostatic attraction between positively charged readily reducible fluorinated cations and the negatively charged anode. This method enables the formation of a robust fluorine-rich interfacial layer with minimal additive (as low as 1 mM) facilitating dense\, conformal Li deposition. This strategy can offers a cost-effective\, environmentally friendly solution for enhancing high-energy batteries.” \n\n\n\n\n\nContact details:Aysel Sarzosa (asarzosa@ic.ac.uk) or Chris Mellor (c.mellor@imperial.ac.uk). \n\n\n\nIn addition\, if you have caring responsibilities that may affect your ability to attend\, take a look at our Conference Care Fund and get in touch to see how we can support you to attend. \n\n\n\n\n\n\n\n\n\nOrganised by:\n\n\n\nDr Maryam Mansoori Kermani\, MSCA Fellow at Imperial College London with the support of AIchemy Hub – Project Team.
URL:https://aichemy.ac.uk/event/applications-of-ai-for-catalysis-and-energy-materials-discovery/
CATEGORIES:Symposium,Workshop
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DTSTART;TZID=UTC:20260402T100000
DTEND;TZID=UTC:20260402T170000
DTSTAMP:20260531T203850
CREATED:20260120T173007Z
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SUMMARY:Machine Learning for Experimental Materials Data
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE2nd April 2026 TIME10:00 – 17:00 Registration fee£0 – IOP & Associate Member      £15 – Student Non-Member £30 – Non-Member Registration Deadline20th March 2026 \n\n\n\nRegisteration now closed\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\ninstitute of physics37 Caledonian Rd\, London N1 9BU \n\n\n\n\n\n\n\n\nPlease note: This event has now passed \n\n\n\nJoin our IOP–AIchemy co-sponsored mini-symposium on machine learning for experimental materials data. This event will unite experimentalists and computational scientists to identify shared challenges\, exchange practical solutions\, and showcase how ML accelerates analysis across scattering\, microscopy\, tomography\, and spectroscopy; driving faster\, more insightful materials discovery. \n\n\n\nThis event is open to everyone\, whatever your background — whether you’re a practising physicist or simply curious to explore the subject. \n\n\n\n\n\nSpeakers:\n\n\n\n\nSam Cooper\, Imperial College London\n\n\n\nKim Jelfs\, Imperial College London\n\n\n\nAndrew McClusky\, University of Bristol\n\n\n\nDylan Owen\, University of Birmingham\n\n\n\nRob Palgrave\, University College London\n\n\n\nShijing Sun\, University of Cambridge\n\n\n\nSarah Haigh\, University of Manchester\n\n\n\nShelley Conroy\, Imperial College London \n\n\n\n\n\n\nPoster Abstracts \n\n\n\nWe invite contributions for poster presentations. If you are interested in presenting a poster\, please submit a short abstract (max 250 words) by email to claire.garland@iop.org by 20 March 2026. Posters will be A0 in portrait orientation. \n\n\n\n\n\nOrganised by:\n\n\n\n\nKelvin Wong\, Post Graduate Teaching Assistant – University College London\n\n\n\nKeith Butler\, Associate Professor in Computational Materials Chemistry – University College London\n\n\n\nAron Walsh – Professor at Imperial College & CSO at CuspAI
URL:https://aichemy.ac.uk/event/machine-learning-for-experimental-materials-data/
CATEGORIES:Symposium,Workshop
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DTSTART;TZID=UTC:20260127T090000
DTEND;TZID=UTC:20260127T170000
DTSTAMP:20260531T203850
CREATED:20250929T131708Z
LAST-MODIFIED:20260223T111247Z
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SUMMARY:New Starters in AI for Chemistry
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE27 January 2026 TIME09:00 – 17:00 COSTFREE REGISTRATION DEADLINE23rd January 2026 \n\n\n\nView Agenda\n\n\n\nread the highlights blog\n\n\n\n\n\n\nEVENT LOCATION\n\n\n\n\nUNIVERSITY OF LEEDSCLOTH HALL COURT\, QUEBEC ST\, LS1 2HA \n\n\n\n\n\n\n\n\nJoin us for a one day Symposium at Cloth Hall Court\, University of Leeds where we aim to bring together recently appointed academics\, postdoctoral researchers and late-stage PhD students who are developing and deploying AI at the chemistry interface.We welcome researchers from all backgrounds across the physical sciences. Our aim is to build a supportive and inclusive community where you can learn\, share experiences\, and explore topical areas such as molecular and materials design\, generative models\, robotics and automation for autonomous experimentation\, data-driven simulation\, and green and sustainable chemistry.AI and ML is opening new frontiers across the chemical sciences from accelerating materials discovery\, to enabling sustainable reactions and autonomous experimentation. If you’re ready to join the community\, and be part of shaping the future of AI in chemistry\, this is the place to begin.  \n\n\n\n\n\n\n\nWho Should Attend: \n\n\n\nThe event will be open to all but is ideally suited to academic staff\, postdoctoral researchers and PhD students from a range of backgrounds and operating at the AI for Chemistry interface.Early-career academics are encouraged to submit applications for oral presentations.Postdoctoral researchers are encouraged to submit applications for an oral presentation or a poster presentation\, depending on the stage of the work that will be presented..PhD students are encouraged to submit applications for poster presentations. \n\n\n\n\n\nConfirmed Speakers\n\n\n\n\n\nDr Gabriella PizzutoUniversity of Liverpool\n\n\n\n\n\nDr Sam Parkinson Aston University\n\n\n\n\n\nDr Emma King-Smith University of Edinburgh\n\n\n\n\n\nDr Michael TilbyUniversity of Bristol\n\n\n\n\n\n\n\nDr Angelo FreiUniversity of York\n\n\n\n\n\nDr Xenfon EvangelopoulosUniversity of Liverpool\n\n\n\n\n\nDr Joe Marsden University of Leeds\n\n\n\n\n\nDr Elenora RicciUniversity of Edinburgh\n\n\n\n\n\nDr William RobinsonRadboud University\n\n\n\n\n\n\n\nContact Details\n\n\n\nFor questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk. \n\n\n\n\n\nThe Organising Committee\n\n\n\nAIchemy HubUniversity of LeedsDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Adam ClaytonDr Gilian Thomas
URL:https://aichemy.ac.uk/event/new-starters-in-ai-for-chemistry/
CATEGORIES:Symposium
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