AIchemy’s Monthly Webinar Series – March 2025

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

Speakers: 

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
Tenure-Track Assistant Professor
École Polytechnique Fédérale de Lausanne | EPFL

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.

We look forward to having you attend the event!