Draft:Acceleration Consortium

• Comment: Has this group been covered in-depth by independent, reliable sources? LittlePuppers (talk) 06:51, 25 October 2026 (UTC)

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Acceleration Consortium

Established	April 20, 2021
Budget	$250 million
Field of research	Materials science
Director	Alan Aspuru-Guzik
Affiliations	University of Toronto, University of British Columbia
Website	https://acceleration.utoronto.ca

The Acceleration Consortium (AC) is a research consortium based at the University of Toronto focused on accelerated materials and molecular discovery using "self-driving labs," robotic research workflows that use artificial intelligence and advanced computing to autonomously design, run, and characterize experiments.

The consortium is directed by chemist and computer scientist Alan Aspuru-Guzik and develops self-driving labs for applications in medicine, energy, plastics, electronics, and construction. In 2023, the Acceleration Consortium received a $200 million award from the Canada First Research Excellence Fund (CFREF), issued by the Social Sciences and Humanities Research Council (SSHRC), National Science and Engineering Research Council (NSERC), and the Canadian Institutes of Health Research (CIHR).

History

The consortium traces its origins to a 2018 project that was using robotic research workflows for new organic laser materials, which Alan Aspuru-Guzik says was what the AC was originally designed for. The University of Toronto then launched the AC in April 2021 as an Institutional Strategic Initiative. It described the Acceleration Consortium as a global coalition that would use materials acceleration platforms (i.e. self-driving labs) to discover and design new materials.

A significant portion of the Acceleration Consortium's projects come from industry partners and collaborations. Merck KGaA was one of the consortium's first partners. The two organizations collaborated on open-sourcing Merck's Bayesian Back End (BayBE) experiment planner, which the AC's self-driving labs later adopted in their automated workflows.

In April 2025, BASF signed a master research agreement with the University of Toronto, the company's first with a Canadian university. The collaborations cover crop protection, coatings that extend the life of architectural materials, and targeted drug delivery. One project, led by formulations lab co-lead Frank Gu, uses plant-derived biopolymers to regulate soil moisture and to slowly release pesticides or fertilizers near the plant.

AC researchers worked with the AI drug discovery company Insilico Medicine on the 2023 Chemical Science study that used AlphaFold to design a potential liver cancer drug. Aspuru-Guzik is an adviser to Insilico. The two groups later co-authored the 2025 Nature Biotechnology study that used quantum computing and generative AI to design molecules targeting the cancer protein KRAS.

The AC also maintains projects with Sanofi, Amgen, Procter & Gamble, Bristol Myers Squibb, IBM, Amazon Web Services, Thermo Fisher Scientific, Mettler Toledo, Sekisui, and others.

Operations

Network of self-driving labs

The AC built a network of self-driving labs, each focused on one particular class of applications, that collaborate on projects with other SDLs while having a mandate of advancing one particular field. The labs are numbered from SDL 0 to SDL 7. SDLs 0 to 6 are housed at the University of Toronto whereas SDL 7 is at the University of British Columbia.

SDL 0: AI and Automation

SDL 0 focuses on overcoming robotic and equipment bottlenecks in self-driving labs by integrating robot manipulation, computer vision, and learning-based agentic reasoning into a unified autonomy stack. Its projects include a GPU-accelerated multiscale digital twin framework for simulating autonomous lab workflows, a general orchestrator for executing closed-loop experiments, and equipment API integrations.

SDL 1: Inorganic

SDL 1 focuses on designing inorganic materials. It focuses on solution-based, solid-state, and electrochemical synthesis pathways with real-time data collection, adaptive process control, and AI-guided planning to further a property-driven optimization with mechanistic understanding. Its projects include the closed-loop discovery of electrocatalysts for the oxygen evolution reaction (OER) for green hydrogen, an automated electrolyzer workflow for CO2 conversion, high-throughput optimization of aqueous battery electrolytes, and fully automated solid-state synthesis of oxides and alloys.

SDL 2: Organic

SDL 2 builds automated, data-driven platforms to design, execute, and optimize complex organic synthesis, with a focus on pharmaceuticals, catalysis, and functional materials. Current projects include an autonomous liquid-liquid extraction platform for solvent-efficient purification and a self-driving workflow for high-throughput solubility measurement and crystallization studies.

SDL 3: Medicinal Chemistry

SDL 3 accelerates early-stage drug discovery by iteratively designing, synthesizing, and testing small-molecule protein binders to optimize target affinity and selectivity . It evaluates compounds for binding directly from crude reaction mixtures to avoid time-consuming purification.

SDL 4: Polymers

SDL 4 synthesizes, purifies, and characterizes polymer libraries using AI-guided feedback to target user-specified properties, monitoring synthesis in real-time with flow reactors. Its projects include fluorine-free polymers to replace PFAS in electronics, polymer surfactant systems for drug delivery and membrane protein-extraction, sustainable degradation of electronic polymer waste, and the development of better polymer coatings .

SDL5: Formulations

SDL 5 focuses on navigating the combinatorial design space of formulations for healthcare, agriculture, personal care, and other industries. Projects include agrochemicals with better solubility to improve crop yields and reduce run-off, stable mitochondrial formulations for regenerative medicine, personal care products like lotions and body washes, thermal fluids for cooling electronics, and nano medicines for poorly soluble drugs.

SDL 6: Human Organic Mimicry

SDL 6 focuses on developing high-fidelity human tissue models, including organoids and organ-on-a-chip systems built from patient samples . Their goal is to discover drugs, test their toxicity, and reduce reliance on animal testing. Its projects include Bayesian optimization of vascularized tissue models for drug efficacy and toxicity testing, machine-learning optimization of media for cardiac microtissues used in heart cell transplant therapy, and active learning-based optimization of antibody production.

SDL 7: Scale-Up

Unlike the other SDLs, SDL 7 is based at the University of British Columbia. The scale-up lab aims to speed the translation of new molecules and materials from early discovery to real-world deployment by automating scale-up and optimization in organic chemistry and materials science.The lab builds on Project Ada, Canada's first self-driving lab, which Jason Hein, Curtis Berlinguette, and Aspuru-Guzik assembled with an $8 million Natural Resources Canada grant in 2018