aichemy

Inorganic Informatics: From Rules to Embeddings
2025-07-31 09:00 - 17:00 UTC
KEY DETAILS

DATE31 July, 2025 TIME09:30 – 16:30 COSTFree

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EVENT LOCATION

Royal School of Mines, Imperial College London, South Kensington Campus, London SW7 2AZ

This workshop explores the principles and practice of inorganic informatics, focusing on the development of SMACT (https://smact.readthedocs.io) for rule-based and embedding-driven materials discovery.

Agenda

TimeSession09:30 – 10:00Arrivals with tea/coffee & pastries 10:00 – 10:15Opening and background – Aron Walsh10:15 – 10:45SMACT repository overview – Anthony Onwuli * Github issues* Documentation 10:45 – 11:15Ongoing work at UCL – Keith Butler 11:15 – 12:00Ongoing work at ICL – Kinga Mastej12:00 – 12:30Future developments and afternoon actions12:30 – 13:30Lunch with tea/coffee13:30 – 16:30Hands-on coding session

Contact details:

For questions related to this event please contact the Aron Walsh or Keith Butler.

AIchemy’s Monthly Webinar Series – July 2025
2025-07-23 14:00 - 15:00
KEY DETAILS

DATE23 July 2025 TIME14:00 – 15:00 COSTFree

REGISTration closed

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

This month’s talks:

Professor 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.

Sriram Vijayakrishnan – Autonomous Mobile Robots For Exploratory Synthetic Chemistry

Autonomous 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.

Following the presentations, there will be time for questions from the audience.

Speakers

Jason Hein Professor of Chemistry

Sriram Vijayakrishnan Postdoctoral Researcher

Gabriella Pizzuto – Webinar Chair Lecturer (Assistant Professor)

(Gen)AI in Chemistry Education Workshop
2025-07-21 10:30 - 16:30
KEY DETAILS

DATE21 July, 2025 TIME10:30 – 16:30
with optional networking drinks until 18:00 COST£25

Registration closed

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EVENT LOCATION

Chemistry Department, South Kensington Campus, Imperial College LondonChemistry / Sir Ernst Chain Building 7 Imperial College Rd, South Kensington, SW72AZ

Please Note: This event has passed and applications are now closed.

Overview:

This workshop is being run by the AIchemy Hub, in association with the teaching team from Imperial College Department of Chemistry. We will explore how (Gen)AI is starting to change the landscape of undergraduate chemistry education, and discuss its applications in chemistry teaching, alongside the benefits and challenges associated with its use.

While some Chemistry departments have been early adopters in using (Gen)AI in chemistry teaching, we are aware of others that are yet to incorporate (Gen)AI into their curriculum. This workshop will therefore provide case study examples while also providing opportunities for wider discussion on the topic.

The morning sessions will outline how (Gen)AI is being used in teaching at Imperial, including showcasing its benefits through guiding experiments, and the challenges associated with using (Gen)AI in chemistry. The afternoon sessions will cover how other chemistry departments are using AI (through invited presentations) and allow time for group discussion on the use of AI in education (chemistry and beyond).

The workshop has limited spaces and due to the potential interest across all chemistry departments in the UK, we have set up an application process to ensure all chemistry departments have the opportunity to send a representative.

Application Process:

Applications are open to all interested individuals involved in chemistry education.

There will be the opportunity to submit an abstract, alongside the application, for those who would like to present how their department is using AI in chemistry teaching. Presentation time approx. 10 mins depending on numbers wishing to present.

Application deadline is has been extended til Friday 6th June 2025.

The expectation is that all universities will be guaranteed at least one place at the workshop (space permitting).

Application outcomes disseminated in mid-June, along with confirmations to those selected to present. A payment link will be sent to those whose attendance is confirmed. Payment is required to secure your place. If payment is not received by 30th June, the place may be offered to another applicant.

Cancellation policy – If you are no longer able to attend after confirmation, please notify us as soon as possible so your place can be offered to another department.

Should space become available, those universities selected to present, will have the first opportunity for an additional representative to attend.

Remaining places will be allocated randomly.

Contact details:

For questions related to this event please contact the AIchemy project management team at info@aichemy.ac.uk.We are delighted to welcome you to the first talks in the AIchemy Hub’s monthly webinar series.

The Organising Committee:

Alchemy HubDepartment of Chemistry (Imperial)Dr Ben Alston (University of Liverpool) Dr Chris Mellor (Imperial) Caroline Woods (University of Liverpool)Aysel Sarzosa (Imperial) Dr Benji Fenech Salerno Dr Rebecca L. Jones Dr Giuseppe Mallia Dr Ravi Singh

AIchemy Frontier Fund Launch Webinar
2025-06-18 13:00 - 14:00
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DATE18 June, 2025 TIME13:00 – 13:30 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube link below to watch the webinar.

Frontier Fund Launch Webinar

Join us for the Launch Webinar of Our New Funding Call

We’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.

Whether 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.

All relevant information about the funding call will be covered during the session

Speakers

Kim Jelfs AIchemy Hub Co-Director

Andy Cooper AIchemy Hub Co-Director

Pint of Science @ Liverpool
2025-05-21 18:30 - 21:00
KEY DETAILS

DATE21 May, 2025 TIME1830 – 21:00 COST£5

SOLD OUT

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EVENT LOCATION

Frederiks32 Hope Street,
Liverpool L1 9BX

Join 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.

AIchemy’s Monthly Webinar Series – May 2025
2025-05-21 15:00 - 16:00
KEY DETAILS

DATE21 May, 2025 TIME15:00 – 16:00 COSTFree

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RECORDINGSClick the YouTube links below to watch each session.

Approximate Bayesian Inference for Spectroscopy with Varying Pure Component Signals Development of ML methods for reaction optimization and prediction

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

This month’s talks:

James Odgers – Approximate Bayesian Inference for Spectroscopy with Varying Pure Component Signals

Spectroscopy 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.

Prof. Abigail Doyle – Development of ML methods for reaction optimization and prediction

Machine 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.

Following the presentations, there will be time for questions from the audience.We look forward to having you attend the event!

Speakers

James OdgersPhD Student

Abigail DoyleProfessor of Chemistry

Keith Butler – Webinar Chair Associate Professor in Computational Materials Chemistry

AIchemy’s Monthly Webinar Series – April 2025
2025-04-23 14:00 - 15:00
KEY DETAILS

DATE23 April, 2025 TIME14:00 – 15:00 COSTFree

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RECORDINGSClick the YouTube links below to watch each session.

Efficient Data Collection for Efficient Decision Making in Process Development Don’t throw your data away! Gaining meaningful insights from high-throughput chemistry

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

This month’s talks:

Linden Schrecker – Efficient Data Collection for Efficient Decision Making in Process Development

Reaction 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.

Over 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.

In 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

Nessa Carson – Don’t throw your data away! Gaining meaningful insights from high-throughput chemistry

With 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

Following the presentations, there will be time for questions from the audience.

We look forward to having you attend the event!

Speakers

Linden SchreckerFounder and CEO, SOLVE Chemistry

Nessa CarsonDigital Champion for Pharmaceutical Sciences, AstraZeneca

Alex Ganose – Webinar Chair Senior Lecturer, Imperial

AIchemy’s Monthly Webinar Series – March 2025
2025-03-19 14:00 - 15:00
KEY DETAILS

DATE19 March, 2025 TIME14:00 – 15:00 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube links below to watch each session.

High-throughput robotic chemical titration using computer vision Teaching Language Models to SpeChemistry: From Design to Synthesis

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

This month’s talks:

Yuan Li – High-throughput robotic chemical titration using computer vision.

A 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.

Prof. Philippe Schwaller – Teaching Language Models to Speak Chemistry: From Design to Synthesis

Artificial 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.

Following the presentations, there will be time for questions from the audience.

Speakers

Yuan LiPhD student

Philippe SchwallerAssistant Professor

Gabriella Pizzuto – Webinar Chair Lecturer (Assistant Professor)

AIchemy Inaugural Annual Conference - March 2025
2025-03-17 09:30 - 17:00 | Molecular Sciences Research Hub, 82 Wood Lane, London, London, W12 0BZ, United Kingdom
KEY DETAILS

DATE17 March, 2025 TIME09:30 – 17:00 COST£25

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EVENT LOCATION

Molecular Sciences Research Hub, Imperial, White City Campus82 Wood Lane, London W12 0BZ

Our inaugural conference aims to bring together experts across different scientific disciplines to explore and discuss the challenges and opportunities for AI in Chemistry. The conference will have short talks and panel discussions covering four key themes:

Optimisation

Generative AI / Large Language Models

Human in the Loop / Robotics

AI tools for Chemistry

Why Attend?

Gain insights from leading experts in AI and Chemistry

Engage in thought-provoking discussions

Network with peers, researchers, and industry professionals

Present your research through poster presentations

Enjoy a full day of learning, collaboration, and innovation

Registration & Tickets

We aim to make this event accessible to all. Tickets are available at a low price of £25, covering the full day of talks, panel discussions, networking opportunities, and refreshments.

Agenda

9:30 – 10:00Registration + Coffee10:00 – 10:15Introductions10:15 – 11:15Optimisation talks and panel discussion11:15 – 11:45Coffee break + networking11:45 – 12:45Generative AI / LLM talks and panel discussion12:45 – 13:45Lunch and networking13:45 – 14:45Human in the Loop / Robotics talks and panel discussion14:45 – 15:15Coffee break + networking15:15 – 16:15AI Tools for Chemistry talks and panel discussion16:15 – 16:45Responsible AI session16:45 – 17:00Wrap up + End17:00 – 18:00Networking Drinks

Join us for a day of AI innovation, networking, and inspiring talks by top experts in the field!

Speakers

Andrew Fowler, Senior Researcher, Microsoft Research

Gabriella Pizzuto, Lecturer (Assistant Professor) in Robotics and Chemistry Automation, University of Liverpool.

Gerit Brandenburg, Head of Digital Chemistry, Merck Group

James Gin-Pollock, Co-Founder & CTO, Orbital Materials

Jeyan Thiyagalingam, Head of AI for Science – Scientific Computing Department, Rutherford Appleton Laboratory, Science and Technology Facilities Council (STFC-RAL)

Luis Figueredo, Assistant Professor, School of Computer Science, University of Nottingham

Kenji Takeda, Director of Research Incubations, Microsoft Research

Kevin Jablonka, Principal Investigator, Helmholtz Institute for Polymers in Energy Applications Jena

Richard Bourne, Professor of Digital Chemical Engineering, University of Leeds

Rob Palgrave, Professor of Inorganic and Materials Chemistry, University College London

Ruth Misener, Professor in the Computational Optimisation, Imperial College London

Sam Cooper, Reader (Associate professor) in Machine Learning for Materials Design, Imperial College London

Sophia Yaliraki, Professor of Theoretical Chemistry, Imperial College London

Yulan He, Professor at the Department of Informatics, King’s College London

Poster presentations

Abstracts are welcome for poster presentations, these should include the title, author names and affiliations, and a description of the research you wish to present – abstracts should be no more than one page of A4 in length. Please mark on your registration form if you wish to present a poster and submit your abstract to info@aichemy.ac.uk by Friday 28 February 2025. You will be notified if your poster has been accepted

AIchemy’s Monthly Webinar Series – February 2025
2025-02-19 14:00 - 15:00
KEY DETAILS

DATE19 February, 2025 TIME14:00 – 15:00 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube links below to watch each session.

Data-driven Materials Discovery Machine Learning Potentials: Beyond Potential Energy

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

This month’s talks:

Dr. Fadwa El Mellouhi – Data-driven Materials Discovery

The 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.

Ross Urqurhart – Machine Learning Potentials: Beyond Potential Energy

Machine 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.

Following the presentations, there will be time for questions from the audience.

We look forward to having you attend the event!

Speakers

Fadwa El MellouhiSenior Scientist

Ross UrqurhartResearch Postgraduate

Keith Butler – Webinar ChairAssociate Professor in Computational Materials Chemistry

AIchemy’s Monthly Webinar Series – January 2025
2025-01-22 15:00 - 16:00
KEY DETAILS

DATE22 January, 2025 TIME15:00 – 16:00 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube links below to watch each session.

So, you want to build a self-driving lab?

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

This month’s talks:

Dr. Sterling Baird – So, you want to build a self-driving lab?

The 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.

Dr. Austin Mroz – Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Consideration

Multi 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.

Following the presentations, there will be time for questions from the audience.

Speakers

Sterling BairdDirector, Training and Programs

Austin Mroz Postdoctoral Fellow

Keith Butler – Webinar Chair Associate Professor in Computational Materials Chemistry

AIchemy’s Monthly Webinar Series – December 2024
2024-12-11 14:00 - 15:00
KEY DETAILS

DATE11 December, 2024 TIME14:00 – 15:00 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube links below to watch each session.

Machine Learning hits the Lab: Experiment Planning with Bayesian (Co-)Pilots Analysing Small Angle X-Ray Scattering Data with Neural Network Accelerated Monte Carlo Sampling

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

This month’s talks:

Prof. 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.

Speakers

Felix Strieth-KalthoffProfessor of Digital Chemistry

Kelvin WongDoctoral Researcher

John Ward – Webinar Chair Senior Lecturer

AIchemy’s Monthly Webinar Series - November 2024
2024-11-20 13:00 - 14:00
KEY DETAILS

DATE20 November, 2024 TIME13:00 – 14:00 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube links below to watch each session.

Self-Optimising Approaches for Flow Synthesis HypBO: Accelerating Black-Box Scientific Experiments Using Experts’ Hypotheses

We are delighted to welcome you to the next session of our AIchemy Hub’s monthly webinar series.

This month’s talks:

Dr. Adam Clayton – Self-Optimising Approaches for Flow Synthesis

Dr 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.

Abdoulatif Cisse – HypBO: Accelerating Black-Box Scientific Experiments Using Experts’ Hypotheses

Abdoulatif 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.

Following the presentations, there will be time for questions from the audience.

Speakers

Dr. Adam Clayton Associate Professor

Abdoulatif Cisse Research Postgraduate

Tahereh Nematiaram – Webinar Chair Strathclyde Chancellor’s Fellow

AIchemy's Inaugural Monthly Webinar
2024-10-23 14:00 - 15:00
KEY DETAILS

DATE23 October, 2024 TIME14:00 – 15:00 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube links below to watch each session.

Automation and Flow: Opportunities for Supramolecular Chemists Streamlining the Discovery of Porous Organic Cages

We are delighted to welcome you to the first talks in the AIchemy Hub’s monthly webinar series.

This month’s talks:Prof. Anna Slater – University of LiverpoolTalk Title: Automation and Flow: Opportunities for Supramolecular Chemists

Professor 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.

Annabel Basford – Imperial College LondonTalk Title: Streamlining the discovery of porous organic cages

Annabel 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.

Speakers

Prof. Anna Slater Professor of Chemistry

Annabel Basford Research Postgraduate

Becky Greenaway – Webinar Chair Senior Lecturer

AIchemy Digital Launch Event
2025-09-05 12:00
KEY DETAILS

DATE5 September, 2024 TIME11:00 – 12:30 COSTFree

REGISTRATION CLOSED

RECORDINGSClick the YouTube links below to watch each session.

Introduction to AIchemy Hub Forerunner Projects AIchemy Hub Activities ECR Committee Funding Opportunities Industry Engagement

Join 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:

Gain insights into AIchemy’s vision and objectives

Discover our research priorities

Hear about the Hub’s activities and events

Find out about funding opportunities

You will also have the opportunity to contribute your suggestions on various aspects of the Hub, helping shape our future programme of activities.

Speakers

Kim Jelfs AIchemy Hub Co-Director

Chris Mellor AIchemy Hub Co-Manager

Andy Cooper AIchemy Hub Co-Director

Ben Alston AIchemy Hub Co-Manager

Graeme Day Forerunner Project 2 Lead

Jacqui Coles Forerunner Project 3 Lead
