AC Social Science & Humanities Research Grants

‍Timeline
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Depending on the number, funding requested, and quality of the applications between 2 and 4 applications will be funded.  
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Purpose of Funding

The Acceleration Consortium social sciences and humanities grants will focus on basic and applied research across all social science and humanities disciplines. The goals of this program include (1) increasing the number of social science and humanities researchers working on questions related to AI-driven discovery using self-driving labs (Accelerated Discovery), see section F for details, (2) promoting the ethical research, production and use of materials and molecules, and (3) using Indigenous, social justice, and other methodologies to consider the benefits and harms of technology deployment. To be in scope, the research project should touch on aspects of the AC’s core mission of ethical and sustainable accelerated discovery and the development of new materials and molecules (substances). Hence, applications could address topics such as the following.

• Understanding what makes a material or substance ethical

• Indigenous and justice-oriented investigations of AI or materials that can support community repair and healing.

• Community-based research design for SDLs and/or ethical substance concerns

• Arts-based approaches to understanding SDLs and ethical substance

• The implications of AI and automation for accelerated discovery and the community-based use of these technologies.  

• The economic, environmental, cultural, and social implications of accelerated materials discovery.

• The development of protocols to ground scientific work in Indigenous and environmental values.

• Building ethical frameworks concerning the benefits and harms of accelerated discovery.  

• Understanding how SDLs alter how scientists contemplate, construct, and think about their research.

• The impact of acceleration on science and product development.

• Policy recommendations to support commercialization and knowledge mobilization.

• The opportunities and threats of low-cost (democratized) SDL technology.

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Funding

• ‍Amount: Maximum $100,000 total, direct research costs‍
• Length: 1-year or 2 -years‍
• Purpose: Increase the number of faculty working on accelerated discovery/SDL related questions, Promote new collaborations, Data generation for larger projects

Note that social science and humanities researchers can also apply to the annual Accelerate Seed, Moonshot, and Translation grants if they need additional resources.

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Selection Process

NOI: Eligible applicants are invited to submit a ~2000 character notice of intents (NOI) that will be used to evaluate project eligibility and identify applications that could be merged (Appendix 1). Projects that are deemed not to be in scope will not be rejected, but a discussion with the project lead will be held to determine if the grant should go to the full application stage.  

At the NOI stage, the quality of the research or team is not evaluated; the NOI is only used to determine if the project is within scope.

Full Application: Successful NOIs will be asked to submit short applications outlining a deliverable-based research project with a timeline and budget, along with CVs for each of the grant’s principal investigators and co-investigators. Applications will be reviewed by the AC’s Scientific Leadership Team’s Awards Sub-Committee and additional experts as identified by the Scientific Awards Sub-Committee.  

Applications will be assessed against the criteria listed in the appendixes.

Grant Requirements & Eligibility

Applicants

• Lead principal investigators must meet the University of Toronto’s eligibility guidelines and be eligible to hold tri-agency funding and identify as social science or humanities researchers.

• Principal investigators from physical or life science disciplines can be co-applicants

• Principal investigators from Canadian universities can be co-investigators and can receive funding via an inter-institutional agreement.

• PIs can only submit one application as a lead applicant.

• PIs leading a current (2024) AC grant will not be eligible to apply for a 2025 grant as a lead.  

• AC Staff Scientists are eligible co-applicants (procedurally, funding would flow to their lab).  

• We welcome and encourage investigators from all career stages to apply, including early career researchers

• Successful applicants will become full members of the Acceleration Consortium and may be asked to support adjudication of future award competitions.

Funding

• All funds are to be spent at the participating academic institution(s) except as noted in the submitted budget  

• Eligible expenses must align with U of T's Guide to Financial Management  and CFREF eligibility guidelines. Please see the CFREF Financial Administration Guide  

Ethical Discovery

• AC research should ultimately support the goals of economic, environmental, and social sustainability and/or advance human health. Technologies developed with AC funding should be evaluated for the potential to cause indirect harm, create positive community impact, and be sustainable throughout its complete lifecycle. These aspects can be part of or a focus of an AC research project.

Intellectual Property

• The AC supports open science approaches for early-stage research and encourages applicants to publish all data sets, software, and blueprints, particularly for the development of platform technologies such as SDL. However, open approaches are not required for all grants.

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Recipient Requirements

The following requirements align with our responsibilities to the CFREF funding bodies:

• At the end of the funding period, sign off on the report of budget expenditures report provided by departmental administrative staff.

• Completion of a short report describing the progress against the objectives and milestones and the research and training impact achieved with the funding, including trainees hired, publications, leveraged funding, etc.

• Report on the research impact achieved from the grant until the end of the CFREF funding period (2024) via a short online survey. Metrics include, but are not limited to publications, patents, startups, leveraged funding, and progress to EDI goals.

• Support the University of Toronto’s and AC’s EDI principles, programs, objectives and reporting obligations.

• Adhere to the Acceleration Consortium’s goal that all materials developed should support the public good. This means that research projects funded by the AC should consider, to the extent that it is possible, the ultimate social, economic, and sustainability of the materials or molecule being researched and any potential indirect harms these materials or molecules could have.  

• Acknowledgment of the Acceleration Consortium and CFREF in all presentations and publications. This assists us in measuring the impact of our programs and supports future funding.

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This research was undertaken thanks in part to funding provided to the University of Toronto's Acceleration Consortium from the Canada First Research Excellence Fund

• Grant number - CFREF-2022-00042

• All applicants (both lead and co-PIs) are required to answer a self-identification survey if they have not already submitted as part of another AC program. In completing this survey, applicants may voluntarily self-identify in all applicable groups, or they may select “prefer not to answer” in response to any of the questions. Self-identification data is important to the University’s ability to accurately identify barriers to inclusion and to develop strategies to eliminate these barriers. Any information directly related to you is confidential and cannot be accessed by the reviewers or the AC team.

About the Acceleration Consortium

To create a healthier, sustainable future, we need better materials. The Acceleration Consortium (AC) is transforming the discovery, design, and commercialization of the materials and molecules needed for a sustainable future by developing and deploying autonomous self-driving labs (SDLs) that leverage advances in artificial intelligence (AI) and automation. SDLs allow scientists to start by defining the material properties they want (e.g., conductivity).  

Materials are not benign, their production, use, and disposal can have significant impacts on the environment and people, both positive and negative. Therefore, as materials discovery becomes rapid, inexpensive, automated, and democratized, the AC is centring the values of ethical, responsible, and sustainable materials discovery. These values can work positively to ensure that the broader impact of materials is considered before they are deployed and that their production is towards collective benefit and sustainability and will leverage commitments to Indigenous approaches to sustainability and governance, including the CARE Principles for Indigenous Data Governance.  

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What is an SDL

Chemical space, the possible permutations and spatial arrangement of atoms to form chemicals and materials, is astronomical. For example, there are an estimated 10⁶⁰ small drug-like molecules alone. Manual approaches and human intuition alone cannot effectively explore this space. By leveraging advancements in AI and automation, SDLs are changing how we do science. SDLs use AI to predict which materials or molecules have desired properties. These predictions are sent to a robotic lab to synthesize, characterize, and assess their properties. AI-based statistical methods are then used to analyze the data and propose the next set of experiments. This cycle continues in an autonomous closed loop until a desired material is discovered. This method is called inverse-closed loop discovery and provides three advantages over traditional experimental or AI-alone approaches, including:  

1. Efficient evaluation: AI models require large datasets and experimental validation, a challenge given the large number of AI predictions. SDLs use AI optimization to explore the vast material space with the fewest number of experiments, finding the optimal solution rapidly and accurately;  

2. Speed: Beyond reducing the number of required experiments, SDLs accelerate the speed of each experiment by leveraging autonomous, high-throughput experiments that run 24/7;  

3. Reproducibility: Using automation with AI control enables high levels of reproducibility and the generation of high-quality data sets.

The AC is developing the SDLs outlined below:

Appendix 1: NOI Application Requirements

Required submission components:  

NOI Package

1. Title of Project

2. Research Team

3. Description of proposed research (2000 characters)

4. Budget Request