Applications of AI for Catalysis and Energy Materials Discovery
2026-05-13 09:00 - 18:00 UTC
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DATE13th May 2026 TIME09:00 – 18:00 COSTFree to Attend

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EVENT LOCATION

Molecular Sciences Research Hub (MSRH), Imperial College London82 Wood Ln, London W12 0BZ

Join 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.

The 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.

The 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.

Who Should Attend:

This symposium is designed for:

Academics and researchers in catalysis, interfaces, and energy materials who are interested in applying AI and machine learning to enhance, accelerate, or expand their research.

AI and machine learning experts looking to apply novel computational approaches to challenges in catalysis, reaction engineering, molecular discovery, and energy‑relevant materials.

Whether 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.

Agenda

TimeSession09:00 – 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

Speakers:

Prof. Evgeny Pidko – Delft University of Technology (TU Delft)

Prof. Fernanda Duarte – University of Oxford, Department of Chemistry

Prof. Jacqui Cole – University of Cambridge

Dr. James Dawson – Newcastle University

Prof. James Durrant – University of Oxford

Prof. Maria Lukatskaya – ETH Zürich

Dr. Ricardo Grau‑Crespo – Queen Mary University of London

Dr. Qilei Song – Imperial College London

Talk Abstracts:

Designing materials for thermoelectric applications: density functional theory and machine learning

Designing 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.

Optical Spectroscopic Analysis of Metal Oxide: Electrolyte Interfaces for Photo- and Electro-Catalysis

I 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.

Interfacial Design in Next-Generation Solid Electrolytes

Interfaces 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.

Data-driven Materials Science for Energy-Sustainable Applications

This 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

Engineering Local Chemical Environments in Electrolytes for Efficient Batteries

“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.

First, 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.

Second, 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.”

Contact details:Aysel Sarzosa (asarzosa@ic.ac.uk) or Chris Mellor (c.mellor@imperial.ac.uk).

In 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.

Organised by:

Dr Maryam Mansoori Kermani, MSCA Fellow at Imperial College London with the support of AIchemy Hub – Project Team.

AIchemy’s Monthly Webinar Series – May 2026
2026-05-20 14:00 - 15:00
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DATE20th May 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams

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We are delighted to welcome you to our AIchemy Hub’s monthly webinar series.

This month’s talks:

Prof. Bao Nguyen – University of Leeds

Talk Title: Who’s learning from whom? Beyond the black boxes of chemical models.

Artificial 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.

He 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.

Nikola Radulov – University of Liverpool

Talk Title: FLIP: Flowability-Informed Powder Weighing

Autonomous 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.

Speakers

Prof. Bao Nguyen Physical Organic Chemistry

Nikola RadulovEarly Career Research

Dr. Adam ClaytonAssociate Professor

ECR Exchange: Scotland 2026
2026-06-02 09:00 - 17:00
KEY DETAILS

DATE2 JUNE 2026 TIME09:00 – 17:00 COSTFREE ABSTRACT SUBMISSION DEADLINE5 MAY 2026 REGISTRATION DEADLINE22 MAY 2026

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EVENT LOCATION

UNIVERSITY OF STRATHCLYDETechnology and Innovation Centre, 99 George Street, Glasgow, G1 1RD

The AIchemy ECR Exchange is primarily aimed at Early Career Researchers across the UK, including PhD students and postdoctoral researchers working at the intersection of AI and Chemistry. While Scotland-focused, the event welcomes participants from further afield who are keen to contribute to and engage with a growing research community. This Exchange is designed to connect emerging researchers, create space for meaningful networking, and encourage open knowledge sharing. By bringing together ECRs working across disciplines, the event aims to spark new collaborations and lay the groundwork for future collaboration opportunities.Through engagement with industry speakers and partners, attendees will gain insight into real-world challenges and translational pathways, helping to shape and focus their research and future career development.

Call for Lightning Talk Abstracts

We invite submissions of oral and poster abstracts from researchers working across all areas at the intersection of AI and Chemistry, including interdisciplinary and applied research. This is an opportunity for ECR’s, PhD students and postdoctoral researchers to showcase their work to a focused audience of peers, academic leaders, and industry representatives working at the AI and chemistry interface. Selected applicants will be invited to deliver an oral presentation as part of the main programme, providing a platform to share their research, receive feedback, and spark potential collaborations. There will also be the opportunity to present a poster, creating additional space for discussion, networking, and deeper engagement with attendees. Please note: Deadline for Submissions has now passed

Who Should Attend:

The AIchemy ECR Exchange is primarily aimed at Early Career Researchers, including:

PhD students

Postdoctoral researchers

Early Career Independent Academic

The event is relevant but not limited to those interested in the application or development of AI for accelerated chemistry and materials discovery. Participation is open to researchers from across the UK who are keen to collaborate and engage with Scotland’s research community.

What you’ll gain:

Opportunities to present and discuss their research

Dedicated networking to encourage future collaborations

Insight from industry speakers on real-world challenges

Exposure to potential pathways for research impact and funding

Invited Speakers

Dr Katerina VrizaPrincipal Scientist at GSK

Dr Joanne CookFormulation Manager at Unilever

Dr Laia Vila-NadalUniversity of Glasgow

Contact Details

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk

The Organising Committee

AIchemy HubUniversity of StrathclydeDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Tahereh NematiaramDr Yashar Moshfeghi

The Great Exihibition Road Festival 2026
2026-06-06 00:00 - 2026-06-07 23:59
KEY DETAILS

DATE6-7 June 2026 TIME12.00 – 18.00 COSTFREE

REGISTRATION CLOSED

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EVENT LOCATION

Exhibition Road, Great Exhibition Road Festival/Marquee – Prince’s GardensSouth Kensington Campus

The Great Exhibition Road Festival is a free annual celebration of science and the arts each summer in South Kensington.

Come visit our Candy Chemistry booth in the Family Fun Zone at the Great Exhibition Road Festival!

Join researchers from Imperial’s Departments of Chemical Engineering, Chemistry, and Computing to explore molecular structure using colourful edible models. Use marshmallows, toothpicks and multicoloured gummies to build molecular structures, then rotate them and see if you can make two structures that are mirror images of each other.

Discover why understanding molecular structure is essential and how AI helps scientists predict and design molecules that shape the world around us.

Just like our left and right hands, molecules may exist in left-handed and right-handed forms that look similar but behave very differently. Changing a molecule’s “handedness” (or chirality) can completely alter its properties, from fragrance to pharmaceutical activity.

AIchemy’s Monthly Webinar Series – June 2026
2026-06-17 14:00 - 15:00
KEY DETAILS

DATE17th June 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams

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We are delighted to welcome you to our AIchemy Hub’s monthly webinar series.

This month’s talks:

Assoc. Prof. Timothy Cernak – University of Michigan

Talk Title: Combinatorial explosion: from atom-bond arrangements to exotic diseases

Chemical 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.

Our 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.

Simone Gallarati – University of Utah

Talk Title: Enabling data-efficient strategies for asymmetric reaction optimization and ligand discovery

In 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

One 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.

References

(1) Gallarati, S.; Bucci, E. M.; Doyle, A. G.; Sigman, M. S. Transferable Enantioselectivity Models from Sparse Data. Nature 2026, 651, 637–646.

(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.

Speakers

Assoc. Prof. Timonthy CernakMedicinal Chemistry

Simone Gallarati Postdoctoral researcher

Speaker Nominations

We 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.

Nominate a speaker

Science in the City: An Evening of AI
2026-06-18 17:00 - 21:00
KEY DETAILS

DATE18 JUNE 2026 TIME17:00 – 21:00 COSTFREE

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EVENT LOCATION

THE SPINE BUILDING2 Paddington Village, Liverpool L7 3FA

Step into the world of AI and see how it’s shaping science, healthcare, and innovation across Liverpool and the UK. From groundbreaking research at the University of Liverpool to real-world applications in our city region, this event brings science to life for a community that takes pride in Liverpool’s innovation, collaboration, and world-class research. Meet researchers and take part in discussions with experts, ask your questions, share your ideas, and see how AI is being used responsibly to make a positive impact. This event is open to everyone, whether you’re a researcher, city resident, alumni or just curious about how AI is shaping the future. Enjoy a welcoming reception before and after the event, the perfect time to meet fellow attendees, spark conversations, and celebrate Liverpool’s thriving research community.Please Note: Registration to this event is essential

What you’ll gain:

Explore how AI is driving breakthroughs in science.

Engage directly with experts in Q&A sessions.

Celebrate and support our city’s research excellence.

No prior experience in AI is required, just curiosity and a desire to be part of the talent, dedication, and pride that Liverpool brings to the forefront of scientific discovery.

Evening Programme:

5:00 – 6:00pm | Arrival & Welcome ReceptionJoin us for a relaxed welcome with refreshments. Meet fellow attendees and start the conversation in an informal setting overlooking Liverpool’s vibrant skyline.6:00 – 8:00pm | AI Across the Sciences – Speaker Sessions & DiscussionHear from leading voices in AI and scientific research as they explore how artificial intelligence is advancing materials chemistry, drug discovery, healthcare, and more. Each session will include opportunities for questions and audience interaction.8:00 – 9:00pm | Conversations & Community ReceptionContinue the discussion over refreshments. Connect with researchers, alumni, and partners, and be part of the growing AI and science community shaping Liverpool’s future.

Speakers

Professor Andy CooperUniversity of Liverpool

Professor Katie AtkinsonUniversity of Liverpool

Dr Alex BattyClatterbridge Cancer Centre

Dr Gabriella PizzutoUniversity of Liverpool

Accommodation and Travel

We are happy to recommend the Novotel Liverpool Paddington Village, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms, breakfast options and easy access to local amenities.

Liverpool is well-connected by rail, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car, parking is available at the Paddington Village Car Park, located close to the University, The Spine Building and the recommended hotel.

Contact Details

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk

The Organising Committee

AIchemy HubDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)

Transitioning from FAIR to AI Ready Data in the Physical Sciences
2026-07-16 09:00 - 17:00
KEY DETAILS

DATE16 JULY 2026 TIME10:00 – 16:00 COSTFREE TALK SUBMISSION DEADLINE12 JUNE 2026 REGISTRATION DEADLINE30 JUNE 2026

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EVENT LOCATION

UNIVERSITY OF SOUTHAMPTONHighfield Campus, Southampton, SO17 1BJ

In recent years, the physical sciences community has been generating increasingly large and complex datasets, at a scale that is now beyond what can be fully explored or analysed by humans alone. As a result, researchers are turning to AI and machine‑learning techniques, which have matured significantly and offer powerful new ways to extract insight from data. However, while the adoption of FAIR data principles has improved data sharing and reuse, experience is showing that FAIR does not necessarily mean AI‑ready. Many datasets remain difficult to use effectively in AI and Machine Learning models.This interactive workshop has been co-created by the Physical Sciences Data Infrastructure (PSDI) and the AI in Chemistry Hub (AIchemy). It aims to bring together researchers, data professional and infrastructure developers to facilitate knowledge exchange and explore what it truly means to be “AI Ready”. The workshop is comprised of invited presentations, lightning talks from participants and interactive discussion sessions. The talks will share current practices, highlighting successes and challenges, and the discussion sessions will explore the practical approaches and tools for evaluating and improving AI readiness.

Who Should Attend:

This in-person event is aimed at anyone interested in dataset standards, curation, and developing robust methods to assess the applicability and reliability of data for reuse. It will be particularly relevant for researchers and research software engineers working with data and AI/ML, data stewards and research data managers, infrastructure and platform developers, and scientists interested in enabling future reuse of their datasets.

Agenda

10:00-10:30Registration & Refreshments10:30-10:35Housekeeping & Welcome10:35-10:45Introduction to PSDI10:45-10:55Introduction to AIchemy10:55-11:10Setting the Scene: From FAIR to AI-Ready11:10-11:25Coffee Break & Networking11:25-12:45Invited Speakers (Matthew Partridge, Aileen Day, Nessa Carson, Otello Roscioni)12:45-13:30Networking Lunch13:30-14:00Participant Lightning Talks14:00-14:15Introduction to Discussion Sessions14:15-14:45Discussion Sessions Part 114:45-15:00Coffee Break & Networking15:00-15:30Discussion Sessions Part 215:30-16:00Feedback & Wrap Up

Call for Lightning Talks

We invite submissions for short lightning talks exploring the challenges, opportunities, and practical experiences involved in creating AI-ready datasets within the physical sciences. This is an opportunity to share emerging ideas, real-world case studies, and lessons learned from working with data intended for AI and machine-learning applications. Topics may include, but are not limited to:

Experiences in developing or curating AI-ready datasets

Challenges in preparing FAIR data for AI and machine learning use

Data quality, metadata, interoperability, and standardisation

Benchmarking, validation, and reproducibilityInfrastructure, tooling, and workflow development

Community needs, open challenges, and proposed solutions

During registration, participants will be able to indicate their interest in presenting a lightning talk. Talk Submission Deadline: 12th June The organising team will review submissions and notify successful applicants by 26th June 2026.

Invited Speakers

Dr Matthew PartridgeUniversity of Southampton

Dr Aileen DayUniversity of Southampton

Dr Nessa CarsonAstraZeneca

Dr Otello RoscioniUniversity of Southampton

Contact Details

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk

The Organising Committee

AIchemy HubPSDIDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Samantha Pearman-KanzaNicola KnightVictoria Hooper

Gen AI In Chemistry Education
2026-07-21 09:00 - 17:00
KEY DETAILS

DATE21 JULY 2026 TIME09:00 – 17:00 COSTFREE

REGISTRATION CLOSED

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EVENT LOCATION

UNIVERSITY OF LIVERPOOLCentral Teaching Labs

This UK national symposium brings together chemistry educators, students, and policy leads to accelerate the integration of Generative AI (GenAI) methods in chemistry education. Building on last year’s event, the focus moves decisively beyond abstract debate toward classroom-ready practice, shared resources, and cross-institutional learning.Aligned with the AIchemy Hub mission, the workshop will advance the UK-wide conversation through three interconnected themes that reflect real teaching, learning, and institutional needs:

Practical implementation of GenAI in chemistry teaching and laboratoriesExplore how AI can be meaningfully embedded into undergraduate teaching and laboratory practice. Sessions will focus on the co-design of AI-integrated experiments, inclusive laboratory tools, and transferable teaching resources. Participants will contribute to shaping a shared repository of AI-enabled laboratory and assessment activities.

AI in assessment, feedback, and curriculum designExamine how AI can act as a co-pilot for educators, supporting assessment design, feedback generation, curriculum review, and bias reduction. This theme supports the development of shared tools and best practice to help bridge the AI–chemistry skills gap for both staff and students.

Institutional strategy, policy, and ethics grounded in real practiceEngage with institution-level strategies and ethical frameworks for the responsible use of GenAI in chemistry education. By comparing approaches across UK universities, this strand will highlight shared principles, practical challenges, and transferable exemplars to support ethical implementation in teaching and assessment.

Call for Lightning Talk Abstracts

We invite submissions for short lightning talks that showcase practical and innovative uses of AI in chemistry education. This is an opportunity to share your practice, ideas, and lessons learned with colleagues from across the UK, and to contribute to a growing national conversation on how AI is shaping chemistry teaching and learning. We particularly welcome abstracts featuring case studies from teaching practice, AI-enabled laboratory experiments, assessment and feedback approaches, curriculum design initiatives, and student–staff co-created projects.During registration, you will be able to indicate your interest in giving a lightning talk and submit a short abstract, after which the organising team will be in touch with further details.

Who Should Attend:

This workshop is aimed at academics working in chemistry with an interest in teaching and education, as well as teachers and staff from colleges and secondary schools who want to better understand future university pathways for their students. It will also be valuable for anyone seeking insight into how artificial intelligence is shaping the next generation of chemistry education across the UK.

What you’ll gain:

Gain insight into the current UK higher-education curriculum landscape for AI in chemistry

Understand how GenAI is being implemented across undergraduate and postgraduate taught courses, in both lectures and laboratories

Explore challenges, risks, and opportunities in real-world adoption

Develop ideas for new lab-based experiments and training using AI methods such as machine learning (ML), convolutional neural networks (CNNs), and Bayesian optimisation (BO)

Speakers

Dr. Ghada RabahNC State University

Dr. Jason SonnenbergOhio State University

Dr. Peter AlstonBPP University

Prof. Kathryn CowtonUniversity of York

Dr. Rebecca JonesImperial College London

Dr. Benji Fenech-Salerno,Imperial College London

Dr. Denise HoughUniversity of Glasgow

Contact Details

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk

The Organising Committee

AIchemy HubUniversity of WarwickUniversity of GlasgowDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr. Chris Mellor (Imperial)Aysel Sarzosa Llerena (Imperial)Tom RitchieDr Dani PearsonDr Ciorsdaidh Watts

Chemical Artificial Intelligence in Homogeneous Catalysis (ChemAICat) Workshop
2026-09-01 09:00 - 2026-09-03 17:00
KEY DETAILS

DATE1 -3 SEPTEMBER 2026 TIME09:00 – 17:00 COST£75 – ACADEMIA £150 – INDUSTRY REGISTRATION DEADLINE3 JULY 2026

Applications closed

agenda

EVENT LOCATION

UNIVERSITY OF LIVERPOOLRendall Building, Seminar Room 4, Liverpool, L69 7WW

About the Workshop

The Chemical Artificial Intelligence in Homogeneous Catalysis (ChemAICat) workshop held at University of Liverpool offers a practical guide to different AI techniques that can be applied to accelerate or complement research in homogeneous catalysis. It is especially recommended for experimental and computational chemists with basic or no Python programming experience. The classes aim to provide these researchers with ready-to-use workflows that they can introduce into their routine research tasks. Some of the protocols covered include:

Basics of Python and cheminformatics

Automated descriptor generation from ChemDraw and CSV files

Data-driven and efficient chemical exploration for catalyst sampling

AI-driven optimization of reaction conditions

AI-driven catalyst discovery

Who Should Attend:

This workshop is aimed at experimental and computational chemists working in homogeneous catalysis with little or no experience in Python. The workshop is suitable for the following career stages: PhD students, postdoctoral researchers and Early Career Academics.

Pre-Requisites:

To help you get the most from the workshop, please ensure the following are completed in advance:

Attendees must bring their own laptops to run the programs used during the workshop. Windows, macOS and Linux operating systems are supported.

Complete a preliminary setup to install the required Python environments. Instructions will be provided a few weeks before the workshop and should take 30-60 minutes to complete.

(Optional) Share custom problems in advance that could be included in the problem-solving sessions. This will allow the workshop to focus on topics that are most relevant to the audience

How to apply

Places are limited and to ensure a balanced mix of expertise and perspectives we are asking applicants to apply. As demand is expected to be high, we ask all interested participants to complete the application form by 3rd July 2026 and decisions will be given to applicants by 10th July 2026.Please note: all bookings are non-refundable.

Accommodation and Travel

Please note that the registration fee does not include accommodation, travel or subsistence. Participants are responsible for arranging their own accommodation and transport during the ChemAICat Workshop.We are happy to recommend the Novotel Liverpool Paddington Village, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms, breakfast options and easy access to local amenities.A social networking event will be hosted on one evening (day to be confirmed) during the school and is included in the registration.For those seeking alternative options, Liverpool offers a wide range of hotels, serviced apartments, and budget accommodations within easy reach of the University.Liverpool is well-connected by rail, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car, parking is available at the Paddington Village Car Park, located close to the University and the recommended hotel.

Airports

Liverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.Manchester Airport (MAN) – Around 1 hour by train or car, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options.

Course Supervisors

Dr. Jamie Cadge (University of Bath)

Dr. Ruben Laplaza (IIQ, University of Seville-CSIC)

Dr. Thijs Stuyver (PSL University)

Dr. Juan V. Alegre-Requena (ISQCH, University of Zaragoza-CSIC)

Contact Details

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk

The Organising Committee

AIchemy HubUniversity of LiverpoolCourse SupervisorsDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Jean-Francois AymeDr. Jamie Cadge (University of Bath)Dr. Ruben Laplaza (IIQ, University of Seville-CSIC)Dr. Thijs Stuyver (PSL University)Dr. Juan V. Alegre-Requena (ISQCH, University of Zaragoza-CSIC)

Transferable Opportunities for AI in Materials Chemistry
2026-09-08 00:00 - 23:59
KEY DETAILS

DATE8 SEPTEMBER 2026 COSTFREE

BOOK NOW

agenda COMING SOON

EVENT LOCATION

The Spine Building2 Paddington Village, Liverpool, L69 7ZD

AIchemy and the EPSRC Programme Digital Navigation for Chemical Space are delighted to host a one-day symposium, Transferable Opportunities for AI in Materials Chemistry, taking place on 8th September 2026 at The Spine, Liverpool.Artificial intelligence is advancing at an unprecedented pace, with many of the most exciting developments emerging from computer science. While these innovations are transforming multiple sectors, there is enormous potential to accelerate the translation of these advances into chemistry and materials discovery. This symposium will bring together experts from computer science to showcase the latest advances in artificial intelligence and foster new interdisciplinary connections with the chemical sciences. By highlighting emerging AI capabilities from across computer science, the programme will encourage attendees to think beyond current applications, explore how these methods could be adapted to chemical and materials research, and spark collaborations that drive future scientific innovation.

What you’ll gain

Discover the latest advances in artificial intelligence emerging from computer science

Explore how transferable AI methods could accelerate chemical and materials discovery

Insights into new computational techniques and emerging AI capabilities

Engage in interdisciplinary discussions with researchers from computer science, chemistry and materials science

Build new collaborations that could shape the next generation of AI-enabled scientific research

Who should attend?

Computer scientists developing AI and machine learning methods

Researchers in chemistry, materials science and chemical engineering

Early Career Researchers and PhD students

Industry scientists interested in AI-enabled materials discovery

Anyone interested in interdisciplinary AI research across chemistry and materials science

Speakers – More to be announced

Dr Vladimir Gusev University of Liverpool

Professor Francesca Toni Imperial

Professor Michael Wooldridge University of Oxford

Dr Louise Dennis University of Manchester

Professor Frans OliehoekTu Delft

Accommodation and Travel

We are happy to recommend the Novotel Liverpool Paddington Village, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms, breakfast options and easy access to local amenities.

Liverpool is well-connected by rail, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car, parking is available at the Paddington Village Car Park, located close to the University and the recommended hotel.

Airports:

Liverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.

Manchester Airport (MAN) – Around 1 hour by train or car, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options.

Contact details:

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk.

The Organising Committee:

Alchemy HubUniversity of LiverpoolDr. Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Vikki Berryman

Data Intensive Science School
2026-09-14 00:00 - 2026-09-16 23:59
KEY DETAILS

DATE14 – 16 SEPTEMBER 26 ACADEMIC PARTICIPANTS£75 INDUSTRY PARTICIPANTS£150

Applications closed

View agenda

EVENT LOCATION

University of Liverpool CampusRendall Building, Liverpool, L69 7WW

Join AIchemy Hub and LIV.INNO for an intensive hands-on Data Science School designed to equip researchers, students, and innovators with practical skills at the intersection of AI, scientific computing, and open-source research.This interdisciplinary programme brings together experts from academia and industry to deliver workshops and talks covering modern data science workflows, scalable computing, machine learning, agentic AI systems, scientific software development, and research reproducibility.Participants will gain practical experience using cutting-edge tools and frameworks while exploring how AI and data-driven approaches are transforming scientific discovery.The school combines hands-on workshops, interactive tutorials, and expert-led talks focused on real-world scientific and data-intensive applications.

Who should apply

This school is designed for:

PhD students and Early Career Researchers

Data scientists and computational researchers

Scientists interested in AI and machine learning

Researchers working with large or complex datasets

Anyone interested in modern open-source scientific computing

What you’ll gain

Participants will:

Gain practical experience with modern data science tools

Learn directly from experts in AI and computational science

Build skills in scalable computing and scientific software development

Explore emerging trends in agentic AI and open-source science

Network with researchers across disciplines

Provisional Programme

Apache Spark Workshop (4 hours) – Learn scalable data processing and distributed computing techniques for handling large scientific datasets.

Machine Learning: Data Collection & Preparatio (4 hours) – Explore the foundations of building robust ML pipelines, from data acquisition to preprocessing and feature engineering.

Agentic AI for Data Analysis (1 hour talk) Discover how autonomous AI agents are reshaping data analysis, scientific workflows, and research productivity.

Git Workshop (2 hours) Develop essential version control skills for collaborative coding, reproducible research, and software development.

Open Source Science (1 hour talk) Examine the growing importance of open-source ecosystems in accelerating scientific innovation and collaboration.

PyAutoFit Workshop (2 hours) Introduction to probabilistic modelling and automated model fitting using PyAutoFit for scientific applications.

Publishing Code (2 hours) Learn best practices for sharing, documenting, packaging, and publishing scientific software and research code.

Real World Challenges Session (2 hours) – Apply your skills to practical scientific and data-driven problems inspired by current research challenges.

How Not to Make NumPy Slow (2 hours) Improve performance and efficiency in scientific Python workflows through optimisation strategies and vectorised computation.

Speakers & Trainers

Dr Edward BennettSwansea University

Dr Prakriti KayasthaUCL

Dr Bradley MartinUCL

Dr James NightingaleNewcastle University

Louise ButcherSTFC/Hartree

Dr Abdoulatif CisseUniversity of Liverpool

Dr Alex HillUniversity of Liverpool

Professor Reecha SofatUniversity of Liverpool

Accommodation and Travel

Please note that the registration fee does not include accommodation, travel or subsistence. Participants are responsible for arranging their own accommodation and transport during the Data Science School.

We are happy to recommend the Novotel Liverpool Paddington Village, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms, breakfast options and easy access to local amenities.

A social networking event will be hosted on one evening during the school and is included in the registration.

For those seeking alternative options, Liverpool offers a wide range of hotels, serviced apartments, and budget accommodations within easy reach of the University.

Liverpool is well-connected by rail, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car, parking is available at the Paddington Village Car Park, located close to the University and the recommended hotel.

Airports:

Liverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.

Manchester Airport (MAN) – Around 1 hour by train or car, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options.

How to Apply:

Places are limited and to ensure a balanced mix of expertise and perspectives we are asking applicants to apply. As demand is expected to be high, we ask all interested participants to complete the application form by 10th July 2026 and decisions will be given to applicants by 17th July 2026.

Please note: all bookings are non-refundable.

Contact details:

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk.

The Organising Committee:

Alchemy HubLIV.INNODr. Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr. Alex HillNaomi Smith

Demystifying Agentic AI & AI Agents Workshop
2026-09-23 09:00 - 17:00
KEY DETAILS

DATE23 SEPTEMBER 2026 TIME09:00 – 17:00 COST£25 REGISTRATION DEADLINE11 SEPTEMBER 2026

book now

View Agenda

EVENT LOCATION

UNIVERSITY OF LEEDSDepartment of Chemistry, Woodhouse Lane, Leeds, LS2 9JT

Join us for this one day workshop and unlock the potential of AI agents and retrieval-augmented generation (RAG) in chemical science. This interactive, hands-on workshop is designed to bring together researchers from academia and industry to explore how emerging AI approaches can transform chemical research and innovation. Whether you are new to these technologies or already experimenting with them, this event will provide both practical skills and a collaborative space to shape future applications.

Why attend?

Participants will gain:

Hands-on experience with RAG models and agentic AI systems, guided by experts

A practical understanding of real-world challenges when applying these tools to chemistry

Opportunities to collaborate across disciplines, connecting early-career researchers, academics, and industry professionals

The chance to co-develop ideas and proposals with clear industrial relevance

Who Should Attend:

This workshop is open to researchers across academia and industry who are interested in applying AI to chemical science.We particularly encourage:

PhD students

Postdoctoral researchers

Early Career Independent Academic

Principal Investigators and industry professionals to join strategic discussions shaping future challenges and collaborations

What you’ll gain:

The day combines practical training with collaborative innovation:

A hands-on RAG workshop, delivered by Data Revival

A hackathon session focused on building AI agents for chemistry

A sandpit session for PIs and industry to define challenges, methodologies, and collaborative opportunities

Structured discussions to develop clear, proposal-ready ideas with defined outcomes and teams

Contact Details

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk

The Organising Committee

AIchemy HubUniversity of LeedsDr Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Professor Bao Nguyen

Winter School: Robotics and AI for Materials Chemistry 2026
2026-11-23 00:00 - 2026-11-27 23:59
KEY DETAILS

DATE23 – 27 NOVEMBER 26 ACADEMIC PARTICIPANTS£150 INDUSTRY PARTICIPANTS£300

APPLY NOW

agenda COMING SOON

EVENT LOCATION

Novotel Hotel, University of Liverpool Campus3 Paddington Village, Liverpool, L69 7ZD

The Robotics and AI for Materials Chemistry Winter School is a five-day intensive training programme focused on digital and automated chemistry, robotic systems, and AI-driven scientific discovery. Hosted at the University of Liverpool, the school is designed for PhD students and early-career researchers, or those new to digital chemistry and AI, and who want to build their capabilities in this rapidly evolving field. A strong working knowledge of Python is required to fully benefit from the practical sessions and technical content.

This Robotics and AI for Materials Chemistry Winter School will:

Provide essential skills in digital chemistry

Strengthen understanding of AI and machine learning, building on participants’ existing knowledge

Offer hands-on experience with key tools and techniques for automated, intelligent lab environments

Participants will explore a range of cutting-edge topics, including:

Digital twins for robotic chemists

Bayesian optimisation for chemical discovery

AI-driven robotic chemists

Robotic manipulation for lab automation

Simulation of robotic chemists

Computer vision-led chemistry

Multi-modal machine learning for science

Agentic AI-based discovery

Human-in-the-loop robotic discovery

After a hugely successful 2025 Winter School, we’re delighted to bring it back for 2026. Take a look at last year’s programme and highlights to see what to expect.

How to Apply:

Places are limited and to ensure a balanced mix of expertise and perspectives we are asking applicants to apply. As demand is expected to be high, we ask all interested participants to complete the application form by 24th August 2026 and decisions will be given to applicants by 31st August 2026.

Please note: all bookings are non-refundable.

Provisional Programme – Coming Soon

Speakers – More to be announced

Prof. Michael MistryUniversity of Edinburgh

Dr. Lauren Ye SeolUniversity College London

Prof. Andi ZhangUniversity of Warwick

Dr. Chenghao LiuCaltech

Alexandra GessnerAstraZeneca

Prof. Simos GerasimouUniversity of York

Accommodation and Travel

Please note that the registration fee does not include accommodation, travel or subsistence. Participants are responsible for arranging their own accommodation and transport during the Winter School.

We are happy to recommend the Novotel Liverpool Paddington Village, a modern hotel conveniently located within walking distance of the University of Liverpool campus. This hotel offers comfortable rooms, breakfast options and easy access to local amenities.

A social networking event will be hosted on one evening during the school and is included in the registration.

For those seeking alternative options, Liverpool offers a wide range of hotels, serviced apartments, and budget accommodations within easy reach of the University.

Liverpool is well-connected by rail, with Liverpool Lime Street Station approximately a 10-minute walk from the University campus. For those travelling by car, parking is available at the Paddington Village Car Park, located close to the University and the recommended hotel.

Airports:

Liverpool John Lennon Airport (LPL) – Around 30 minutes from the University by taxi or public transport. The airport offers flights to many UK and European destinations.

Manchester Airport (MAN) – Around 1 hour by train or car, with direct rail connections to Liverpool Lime Street. This airport provides a wide range of international flight options.

Contact details:

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk.

The Organising Committee:

Alchemy HubUniversity of LiverpoolDr. Ben Alston (University of Liverpool) Caroline Woods (University of Liverpool)Dr Gabriella PizzutoDr Xenofon EvangelopoulosProf. Alessandro TroisiMinh CaoDr Mengjia ZhuDr Xin Yang

Computational Methods for Defects in Solids Workshop
2027-01-06 00:00 - 2027-01-09 14:30
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