26 April 2022

AI/ML in formulation design – Opportunities and Challenges

Organised by:

SCI's Formulation Forum 

SCI, London, UK

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For scientists, students and young researchers in industry and academia

Curious to learn about Artificial Intelligence and Machine Learning in R&D? AI and ML are promoted as tools that will change the world - in research they promise to deliver powerful new solutions to the discovery and design process. In our future careers in industry and academia, AL and ML will become every-day tools for researchers and having knowledge and understanding of what they could and could not achieve is important for young scientists starting careers in industry. This meeting is for scientists, especially students and young researchers in industry and academia who want to learn more about AI and ML, how this technology might be used and what it could do. The event features talks by experts in academia, industry and from AI/ML companies who apply AI/ML to solve real problems today and in the future. Posters on application of AI and ML welcome.

Artificial intelligence and machine learning are emerging tools that are advancing rapidly and can offer interesting solutions to complex scientific challenges. Promising examples already exist for example in molecule design; however formulation design is a more complex area and this meeting will explore what AI/ML can and cannot offer to the optimisation of formulation design in this multifaceted space.

This event is designed to bring together the artificial intelligence and formulation science communities to explore what opportunities and challenges exist at the AI/ML – formulation design interface.

Posters are welcome on all aspects concerning the application of AL/ML to the design of formulations, encompassing recipe and process design. Please download an abstract template here (maximum 1 A4 page) and send to conferences@soci.org by Thursday 14 April 2022 with the subject line “AI/ML in Formulation Design – Opportunities and Challenges. 



This event will be of interest to industrialists, academics and students working in the fields of AI/ML and/or Colloid and Formulation Science.


Andrei Leonard Nicusan

University of Birmingham

Andrei Leonard Nicusan is a researcher at the University of Birmingham focusing on data-driven engineering across scales. He published featured articles and Scientific Highlights on machine learning-based positron emission particle tracking algorithms. His work on evolutionary algorithms for simulation calibration, optimisation and physics discovery has raised more than £260,000 from research and industrial funding bodies. His frameworks are actively being used in projects with JM, GranuTools, JDE, FMC, Recycling Technologies.

Carl Reynolds

University of Birmingham

Dr. Carl Reynolds is a research fellow in the Energy Materials Group at the University of Birmingham. Carl is interested in using novel metrology to understand industrial problems, particularly involving rheology and complex flow. He has worked with industrial partners including Michelin, BP and Unilever on topics ranging from polymer processing to volcanic eruptions. He is currently part of the Faraday Institution Nextrode project, applying novel techniques to optimise battery electrode manufacture, by elucidating the physical relationships between process parameters and outputs.

Dr. Christoph Kreisbeck


Dr. Christoph Kreisbeck is Chief Commercial Officer at Kebotix and oversees business development, sales, marketing, and product envisioning. Christoph is passionate about disruptive technologies and bringing tough tech to the commercial world. Out of Harvard, he co-founded Kebotix to build the world’s first autonomous materials discovery platform, accelerating the industry's transition to a new era of digital R&D. Between 2014 and 2016, he worked as a software developer within a spearhead project on self-driving cars. As he likes to say: "From self-driving cars to self-driving labs.” Among other achievements, Dr. Kreisbeck is the lead architect of the high-performance software 'GPU-HEOM', which is used by more than 200 scientists worldwide for research on novel design concepts of next-generation solar cells.

Hannah Melia

Citrine Informatics

Hannah Melia is a Product Management Consultant to Citrine Informatics, the world leader in AI for Materials and Chemicals. She studied Materials Science and Metallurgy at the University of Cambridge. Since then she has worked in various technology-based industries in Germany, the UK, and the USA. For the last 13 years she has worked in Materials Information software, first at Granta Design (now Granta Ansys) and now for Citrine.

Prof John Overington

Exscientia plc

John Overington is a VP of Discovery Informatics at Exscientia, and a visiting professor at University College London.

He earned a PhD in Computational Structural Biology from Birkbeck College, University of London. Previously, he was CIO at the Medicines Discovery Catapult, an SVP at Benevolent AI, Head of Chemistry Services at EMBL-EBI, an SVP at Inpharmatica Ltd, and Head of Molecular Informatics Structure and Design at Pfizer. He has published broadly across many areas of drug discovery informatics including ca. 150 publications and been responsible for the development of some of the core foundational resources for machine learning in drug discovery, for example the ChEMBL database.

Dr Laura Filion

Utrecht University

Laura Filion has a masters in physics from McMaster University, Canada, and a PhD from Utrecht University, Netherlands. After working as a post-doc at Cambridge University, UK, she moved back to Utrecht University, where she currently works as an associate professor in soft condensed matter. Her research focuses on using classical statistical physics, computer simulations and machine learning to examine the self-assembly of colloidal particles, both in and out of equilibrium. She is well known for her work on self-assembly in entropy-driven systems (including binary hard-sphere mixtures), defects in colloidal crystals, and crystal nucleation. One of her main research lines currently is the design of efficient and light-weight machine learning methods to aid in the study of soft matter.

Dr Linjiang Chen

University of Birmingham

Linjiang’s research focuses on Data-driven Materials Exploration and Optimization (DaMEO) by fusing chemical knowledge with state-of-the-art computation ranging from quantum mechanics to data-driven heuristics. Linjiang leads the DaMEO group, whose current mission is to build step-change computational capabilities at the intersection of chemistry, chemical engineering, and computer science to redefine our ability to position atoms and molecules for function.

Linjiang was awarded his PhD in molecular modelling from the University of Edinburgh in 2014. From 2013 to 2017, Linjiang was a postdoctoral research associate with Prof Andy Cooper at the University of Liverpool, followed by a research fellow and theme lead position in the Leverhulme Research Centre for Functional Materials Design, until February 2022. In March 2022, Linjiang joined the School of Chemistry at the University of Birmingham, as a lecturer in computational chemistry.

Mark Taylor

CPI High Throughput Informatics and Modelling

Mark has 30 years experience of lab automation, instrumentation development, data management and data analytics gained from the academic, SME and large corporate sectors. At CPI he established a team focusing on application of a number digital technologies in service of industrial formulation, including high throughput experimentation, process analytics and model-based process control, and data analytics. His current role as Chief Technologist is leading the further strategic and collaborative development of these technologies and horizon-scanning for digital technologies that can be applied for benefit of CPI’s industrial customers.

Dr Sebastian Niedenführ

Bayer AG

Project leader & senior data-scientist in biotechnological and chemical R&D with broad spectrum of interests in technical & organic chemistry, biotechnology, analytics and formulation. Currently responsible for shaping future vision & projects for data science`s role in product formulation design together with associated partners.

Tom Whitehead


Tom is Head of Machine Learning at Intellegens, a machine learning spin-out from the University of Cambridge that specialises in handling sparse and noisy experimental data. He joined Intellegens from his PhD in theoretical physics at the University of Cambridge, and is now leading the application of Intellegens' novel deep learning approaches to a wide variety of industrial applications. Tom is interested in developing machine learning approaches to solve previously intractable problems in a variety of scientific and engineering fields including industrial chemistry.


Tuesday 26 April

Registration and Refreshments
Dr Malcolm Faers
Setting the scene – Formulation and AI/ML
Dr Malcolm Faers, Prof Jeremy Frey
What can machine learning approaches potentially do for drug formulation?
Prof John Overington, Exscientia plc
Data-driven materials exploration and optimization: towards de novo design of organo-photocatalysts
Dr Linjiang Chen, University of Liverpool
Autonomously revealing hidden local structures in supercooled liquids
Dr Laura Filion, Utrecht University
Lunch & posters
Formulation and Electrode Designs for Sustainable Batteries
Dr Carl Reynolds, University of Birmingham
AI in action: supporting formulation design projects
Tom Whitehead, Intellegens
ACCES: Autonomous Characterisation and Calibration via Evolutionary Software
Andrei Leonard Nicusan, Formulation Engineering CDT, University of Birmingham
Industry example using AI/ML for formulation design
Dr Sebastian Niedenfuehr, Bayer AG
Refreshment break
How AI is being used by formulators today - and what made it successful
Hannah Melia, Citrine Infomatics
Innovation 4.0: The role of digitalisation in Formulation R&D
Dr Matthew Reeves, KTN Materials Chemistry and Formulation
Case studies in Formulation R&D digitalisation
Dr Mark Taylor, CPI High Throughput Informatics and Modelling
KTN & CPI joint questions
AI accelerated closed-loop Innovation - Data-driven decision-making for formulation design and materials optimization
Dr Christoph Kreisbeck, Kebotix
Networking reception & posters
What does industry want from AI/ML? What can AI/ML offer industry? How can SMEs access AI/ML?
Panel Discussion
Closing talk – Wrap up & future opportunities?
Prof Jeremy Frey, University of Southampton
Due to the uncertainty and ever changing situation regarding the pandemic and company travel policies, it may be necessary for some speakers to present remotely at short notice.
Venue and Contact


14/15 Belgrave Square

Conference Team

Tel: +44 (0)20 7598 1561

Email: conferences@soci.org

Before early bird - ends 31 March 2022
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Non-member - £115
Student member - £30
After early bird
Member - £100
Non-member - £140
Student member - £40

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Organising committee

Dr Malcolm Faers, SCI/ Bayer AG
Prof Paul Bartlett, SCI/ University of Bristol
Prof Jeremy Frey, University of Southampton
Prof Paddy Royal, ESPCI, Paris