The Acceleration Consortium (AC) has awarded nearly $300,000 to explore the societal impacts of self-driving labs (SDLs), which use AI and automation to accelerate materials discovery.

Materials are not benign and to mitigate any unintended harm, the AC is supporting research that evaluates the environmental, economic, social, cultural, and legal consequences of speeding up science. This year’s recipients include Imre Szeman, Timothy Welsh, and Kristina McElheran, who aim to tackle pressing questions surrounding the automation of scientific processes, exploring how SDLs will impact labour, resource governance, and global power dynamics. Each of their research projects will offer insight into the societal implications of these technologies, moving beyond technical assessments to foster a more just, sustainable, and equitable future for both science and society.

This funding is made possible, in part, by the AC’s historic $200 million grant from the Canada First Research Excellence Fund (CFREF). The AC’s social science research grants will be awarded on an annual basis, with the next round of funding opening in January 2026.

While each project approaches their work from a distinct disciplinary perspective, collectively they all examine how AI and automation are fundamentally reshaping the world around us.
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Learn more about the research of our 2025 social science grant recipients:  ‍

Automating Extraction: The Socio-Political Impacts of AI-Driven Materials Discovery

Imre Szeman (Human Geography, U of T), Sergio Montero

Research questions:  

As the world shifts to renewables, there is a growing demand for critical minerals. These minerals are often mined in ways that harm the environment and local communities. SDLs are being promoted as tools that can speed up the discovery of materials that will reduce reliance on scarce resources. But this shift also raises big questions:  

• Who controls these new technologies?  

• How do they change the work of scientists?  

• And what are the wider impacts on supply chains, global power, and resource governance?

Through policy and discourse analysis, case studies, interviews, and geospatial analysis, this project will analyze how SDLs impact trends in resource extraction and global production networks, and affect workers, communities, and the environment.  

Research goals:

The goal is to understand the risks and opportunities of AI-driven materials discovery and, through research and policy-oriented publications, suggest ways to make this process more ethical, just, and sustainable.
 

Equity in Photorealistic Digital Twins for Chemistry and Chemical Engineering Laboratory Education and Automation

Timothy Welsh (Faculty of Kinesiology & Physical Education, U of T), Ariel Chan, Kourosh Darvish, Sterling Baird, Xiaoye Michael Wang

Research questions:  

SDLs integrate machine learning and robotics to accelerate discovery. However, human judgment remains essential for managing ambiguity, contextualizing data, and guiding decision-making. This project investigates how individuals from diverse backgrounds interact with scientific tasks in SDLs using virtual reality (VR) digital twins of SDLs. Key focus areas include:

• Understanding human-computer interaction and the handover of tasks between humans and robots: when transitions occur, how smoothly they unfold, and how shared goals are communicated

• Exploring how individuals from diverse backgrounds perceive, navigate, and interact within photorealistic VR simulations of chemistry and chemical engineering laboratories, capturing workflows from bench-scale to pilot-scale

Research goals:  

The goal is to inform the design of inclusive, human-aware workflows—supporting automation systems that are not only faster and smarter, but also more equitable, intuitive, and adaptable for diverse use.
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Team Composition and Dynamics in Self-Driving Labs

Kristina McElheran (UTSC and Rotman School of Management, U of T) Marlene Koffi, Megan MacGarvie, Sterling Baird and Aaron Clasky

Research questions:

This research explores the human side of accelerated materials discovery, to evaluate how team structure and diversity affect performance in autonomous labs. The team will inquire:

• How do team size and disciplinary diversity influence research outcomes?

• What is the optimal mix of specialists and generalists?

• How are team dynamics affected by increased automation?

Research goals:

This project will establish evidence-based strategies for structuring effective SDL teams, helping academia, industry, and government to optimize research teams to leverage automated discovery while realizing human potential and diversity in scientific research.