8th Machine Learning and AI in Bio(Chemical) Engineering Conference
01-02 July 2025
In person-only event
Machine Learning and AI in Bio(Chemical) Engineering Conference series is co-organised by researchers linked with the Innovation Centre in Digital Molecular Technologies (iDMT), at the University of Cambridge. This series of conferences grew from two collaborative research projects on robotics and automation in chemical development (Glasgow, Leeds, Cambridge, UCL and Southampton). We continue to engage with the growing academic community in digital chemistry, chemical engineering and biochemical engineering, to design each iteration of the conference. The conference will feature keynote and invited lectures from global champions of research in ML/AI in chemistry/(bio)chemical engineering, and regular lectures selected from the community submissions.
Conference Agenda
The event will be held at the Department of Chemical Engineering and Biotechnology,
Philippa Fawcett Dr, Cambridge CB3 0AS
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01 July 2025 (Tuesday), Day One
10:00 – 11:00 Welcome and refreshments
11:00 – 12:00 (Keynote) Kim E. Jelfs (Imperial College London) - Remembering the Lab in Computational Molecular Materials Discovery
12:00 – 12:25 Calvin Yiu (University of Bristol) - Web-IMPRESSION: Graph Transformer Network for Fast, Accessible, DFT Accurate NMR Predictions for Chemical Shifts and Couplings at Your Fingertips
12:25 – 12:50 Speaker 2
12:50 – 14:00 Lunch
14:00 – 14:25 Speaker 3
14:25 – 14:50 Eike Cramer (RWTH Aachen University) - Bayesian optimisation with mechanistic knowledge
14:50 – 15:15 Dasha Semochkina (University of Southampton) - AI-powered bayesian framework for chemical kinetics: Towards efficient experimental design
15:15 – 15:30 Break (Refreshments, Coffee and Tea)
15:30 – 16:00 Nicholas Ballard (The University of the Basque Country/POLYMAT) - Machine learning for polymer reaction engineering
16:00 – 16:25 Leo Vattoly (Basque Center for Applied Mathematics) - Predicting Optimal Particle Morphology Conditions: A Reverse Engineering Methodology
16:50 – 17:15 Malavika Raj (The University of the Basque Country/POLYMAT) - Knowledge-Guided Machine Learning for Acrylic Pressure-Sensitive Adhesives
17:15 – 19:00 Networking and Dinner
End of day 1
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02 July 2025 (Wednesday), Day Two
09:00 – 09:15 Coffee reception
09:15 – 10:10 Keynote Speaker 2
10:10 – 10:35 Lukas Sigmund (AstraZeneca) - Machine learning the site-selectivity of C-H functionalization reactions
10:35 – 11:00 Pedro J. Ballester (Imperial College London) - AI-guided phenotypic virtual screening against Streptococcus pneumoniae
11:00 – 12:30 Poster Session + Break (Refreshments, Coffee and Tea)
12:30 – 13:15 Lunch
13:15– 13:40 Federico Galvanin (University College London) - Autonomous platforms for model identification: The role of optimal experimental design
13:40 – 14:05 Jan-Frederic Laub (ETH Zurich) - Automated Generation of Mechanistic Models for Chemical Processes using Reinforcement Learning
14:05 – 14:30 Stefano Iannello (University College London) - Physics-Informed Neural Networks for chemical recycling of plastics in fluidised bed reactors
14:30 – 14:50 Tea Break
14:50 – 15:15 Speaker 15
15:15 – 15:40 Alena M. Sheveleva, Gregory Price (BIOVIA) - Simplifying the user experience: Application of LLM methods to Formulation Science within BIOVIA applications
15:40 – 16:40 Workshop (Accelerated Materials)
16:40 – 17:00 Closing Remarks & RSC Prize Presentation
End of day 2
Speakers
Keynote Speakers
Professor Kim E. Jelfs
Imperial College London
Remembering the Lab in Computational Molecular Materials Discovery
Professor Garrett M. Morris
University of Oxford
Physics or Stamp Collecting: How Good is AI at Molecular Docking?
Confirmed Speakers
Professor José Miguel Hernández Lobato
(University of Cambridge)
Deep generative models for molecule simulation
Professor Federico Galvanin
(University College London)
Autonomous platforms for model identification: The role of optimal experimental design
Professor Nicholas Ballard
(The University of the Basque Country)
Machine learning for polymer reaction engineering
Dr. Eike Cramer
(RWTH Aachen University)
Bayesian optimisation with mechanistic knowledge
Dr. Dasha Semochkina
(University of Southampton)
AI-powered bayesian framework for chemical kinetics: Towards efficient experimental design
Dr. Lukas Sigmund
(AstraZeneca)
Machine learning the site-selectivity of C-H functionalization reactions
Special Session
ML/AI for Polymer Synthesis and Discovery
CINEMA brings together academic partners—the University of the Basque Country (UPV/EHU), Ghent University, RWTH Aachen, the Basque Center for Applied Mathematics, and the University of Cambridge—and industry leaders including Covestro, Arkema, Synthomer, and BASF. The initiative aims to develop machine learning-based methods for designing and controlling emulsion polymerization products and processes, combining cutting-edge machine learning techniques with fundamental scientific expertise.
Workshop
Making it Modular: Scalable Automation Strategies for Modern R&D
Fully automated and autonomous laboratories are the future of chemical R&D. However, modern laboratories struggle to implement new technologies in AI and robotics fue to constraints in expertise, expense and time. In this workshop, founder and CEO of Accelerated Materials, Nicholas Jose, will cover current opportunites and challenges in autonomous R&D, and introduce modular design methodologies for creating automated yet versatile workflows. Participants will be lead through real world case studies in materials synthesis, formulation and more using AMLearn, the company's application for AI and Machine Learning integration.
Abstract Submission
The 8th International Conference on Machine Learning (ML) and AI in (bio)Chemical Engineering will take place on 01-02 July 2025. We are requesting abstracts for posters and talks related to this theme. Potential topics/submissions include but are not limited to:
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Software implementations of key methods
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Experimental case studies using ML
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Method development, particularly those that simplify user experience
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Benchmarking of ML and AI methods
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AI for polymer development and property predictions
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Machine learning in life cycle analysis and process intensification
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Coupling AI with high-throughput experimentation or robotic platforms
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Synergies between ML and quantum computing for chemical property predictions
Abstracts may be up to 400 words and optionally include explanatory figures. We emphasise that abstracts should include sufficient introduction for newcomers.
The deadline for submissions is 16 May 2025.
Sponsors
