The Acceleration Conference is the world’s largest and premier event for self-driving lab (SDL) researchers focused on accelerating materials and molecular discovery. Hosted annually by the Acceleration Consortium, the conference brings together researchers, industry leaders and technologists at the forefront of lab automation, AI-driven formulation and autonomous experimental platforms.
The 2025 conference was held from August 11 -14, 2025, featuring four days of research showcases, keynote talks, panel discussions and live demonstrations. This year’s program was chaired by ChemE’s Mohamad Moosavi, who remarked, “The Acceleration Conference has become a key venue for advancing self-driving lab research, bringing together diverse perspectives from academia and industry. It was inspiring to see how rapidly the field is evolving and how our community is shaping the future of autonomous science.” The event welcomed more than 400 attendees representing over 19 countries.
Reflecting on the significance of the conference, Professor Frank Gu noted, “Toronto is now viewed as the global hub for self-driving labs for materials discovery. This conference is also relevant to ChemE, as it helped to showcase how chemical engineering is evolving through the integration of robotics, data science, and machine learning.”
The Gu Lab Showcase: Launching RAISE.AI
As participants in the research showcase and technology demo, the Gu Lab unveiled RAISE.AI (Robotic Autonomous Imaging Surface Evaluator AI) for the first time.
RAISE.AI is a self-driving experimental platform that combines robotics, computer vision and machine learning to accelerate the discovery of formulations that interact with surfaces—whether to wet, coat, spread or clean. The system autonomously designs, prepares and tests formulations, then learns from the results in real time to recommend the next best experiment.
“This enables exploration of hundreds of formulation combinations with minimal human input,” says Gu. “Our work highlights the transformation underway in chemical engineering, shifting from manual workflows to AI-guided, closed-loop discovery, where automation and algorithms collaborate to solve complex problems.”
Team Contributions
Participants from the Gu Lab included:
- Mohammad Nazeri – PhD Candidate (RAISE core development)
- Erin Ng – MASc Student (contact lens wettability, RAISE-Vision)
- Sheldon Mei – PhD Candidate (RAISE image processing and orchestration)
- Sara Sadr – Postdoctoral Researcher (automated workflows, materials interface)
- Caleb Johnston – MASc Student (SDL for formulation testing)
- Zion Park – MASc Student (SDL for materials formulation)
- Anya Charyshnikova – Undergraduate Researcher (RAISE for automation workflow)
- Tithi Choksi – Undergraduate Researcher (RAISE automation pipelines)
The team also collaborated closely with staff scientists from SDL teams at the Acceleration Consortium, including:
- Dr. Aaron Clasky – SDL-5, specializing in formulation automation
- Dr. Nasim Abdollahi – SDL expert in formulation and medicinal chemistry
Posters and Research Highlights
The Gu Lab presented both a live booth demo and several research posters on RAISE, showcasing how automated liquid handling, imaging and Bayesian optimization can rapidly evaluate formulation effects on contact angle, spreading and evaporation.
In addition, Charlie Chen (Postdoctoral Fellow with Chris Lawson and Mohamad Moosavi) was awarded a poster prize at the conference, highlighting the strength and visibility of ChemE’s research contributions.
The poster contributions included:
- Robotic Autonomous Imaging Surface Evaluator (RAISE) to Accelerate Material and Formulation Discovery – Mohammad Nazeri, Sheldon Mei, Jeffrey Watchorn, Alex Zhang, Erin Ng, Tao Wen, Abhijoy Mandal, Kevin Golovin, Alán Aspuru-Guzik, Frank Gu
- Integrating Image Processing to Automate Contact Angle Measurements within Liquid-Handling Robot – Sheldon Mei, Mohammad Nazeri, Jeffrey Watchorn, Alex Zhang, Frank Gu
- Contact Lens Wettability using Automated Contact Angle Measurement Platform: RAISE-Vision – Erin Ng, Mohammad Nazeri, Sheldon Mei, Alex Zhang, Tithi Choksi, Victor Hau, Aaron Clasky, Jeffrey Watchorn, Christine Allen, Frank Gu
- Automated Blade Coating, Curing, and Surface Characterization Workflow within a Liquid Handler Robot – Anna Charyshnikova, Tithi Choksi, Mohammad Nazeri, Erin Ng, Frank Gu
Professor Gu notes, “These projects demonstrate how chemical engineering principles—mass transfer, surface science, rheology—can be programmed through automation and machine learning to accelerate formulation and materials design.”
Looking Ahead
The event sparked valuable discussions on future collaborations, including applying RAISE to challenges in agriculture, biomedical materials and energy systems. Interactions with other SDL developers and industry partners focused on a shared challenge: designing autonomous platforms that can operate reliably amid the complexity and variability of real-world chemical engineering systems.
Many faculty members from the department also attended, including:
- Ramin Farnood
- Mohamad Moosavi
- Benjamin Sanchez-Lengeling
- Weilai Yu
- Cathy Chin
- Jay Werber
“This conference reinforces how core chemical engineering principles are being developed into tools like self-driving labs to enable researchers to explore complex formulation spaces,” says Gu.
“The event also highlighted the critical role U of T is playing in shaping the future, where automation, data, and AI are becoming integral to the way chemical engineering tackles real-world challenges.”