1,000+ Opportunities
Find the right grant
Search federal, foundation, and corporate grants with AI — or browse by agency, topic, and state.
AI for Actionable Matter Modelling is a Schmidt Sciences pilot programme seeking AI-driven methods that make reliable, experimentally useful predictions in exactly the regimes where conventional computational approaches break down: multi-scale phenomena, realistic surfaces and interfaces, conformational ensembles, and disordered or dynamic systems.
The target applications are the functional properties of catalysts, semiconductors, drugs, batteries and biomolecules in cases where defects, interfaces and dynamic motion are decisive rather than incidental.
The programme's framing is deliberately narrow and unusually demanding, and understanding it is the key to a competitive proposal: the emphasis is on actionable and experimentally useful prediction, meaning the standard is whether a wet-lab or fab researcher could act on the output, not whether a model achieves a strong score on a curated benchmark.
This positions the programme against a common failure mode in AI-for-materials and AI-for-chemistry work, where methods perform well on idealised crystalline structures or equilibrium configurations and degrade precisely at the defects, interfaces and dynamics that determine real material and molecular function.
Proposals that connect a machine learning advance to a concrete experimental validation path, ideally with an experimental collaborator already engaged, are far better matched than pure methods development. The programme sits within Schmidt Sciences' broader AI for Science portfolio, which also includes AI in Science and Society and related calls. Programme contact is aiforscience@schmidtsciences.
org.
Get a weekly digest of new grants like this
A free weekly digest of new foundation and federal funding opportunities as they're added to Granted. Unsubscribe anytime.
Or search similar grants →According to the current listing, eligibility includes: The programme is open globally to universities and non-profit research institutions, with no restriction to the United States, which makes it one of the more internationally accessible AI-for-science funds. For-profit organizations are not eligible as lead applicants. Indirect costs are capped at 10 percent, so applicants must confirm that their institution will accept the reduced rate before submitting; at research-intensive universities this typically requires a formal waiver from the research office and is the most common administrative obstacle on Schmidt Sciences calls. Applicants choose between Track I, up to 100,000 dollars over six to twelve months for a focused exploratory effort, and Track II, 100,000 to 500,000 dollars over twelve to eighteen months for a more substantial method-development programme; the tracks are judged separately so applicants should choose on the basis of what the work genuinely requires rather than defaulting to the larger band. The substantive filter is the actionability requirement. Work must target regimes where conventional methods fail, specifically multi-scale phenomena, realistic surfaces and interfaces, conformational ensembles, and disordered or dynamic systems, and must produce predictions that are experimentally useful; benchmark-driven methods papers on idealised systems are the characteristic mismatch. No deadline is recorded here because the 2026 cycle closed on 30 April 2026 and Schmidt Sciences has archived the opportunity, with no successor call published as of early October 2026. Because this is described as a pilot programme, a second cycle is plausible but not assured; prospective applicants should monitor the Schmidt Sciences opportunities page and may contact aiforscience@schmidtsciences.org to ask whether a further round is planned. Confirm the full requirements in the official notice before applying.
The current listing shows the programme funds two tracks with distinct budget bands. Track I provides up to 100,000 US dollars for projects of six to twelve months, and Track II provides 100,000 to 500,000 dollars for projects of twelve to eighteen months, with ten to fifteen awards expected across both tracks. amount_min is recorded as 100,000, which is both the Track I ceiling and the Track II floor, and amount_max as 500,000, the Track II ceiling. Indirect costs are capped at 10 percent, which is the most consequential financial detail on this record: a university applicant accustomed to a federally negotiated indirect rate of 50 to 65 percent will find that a 500,000 dollar Track II award yields substantially more direct research funding than a nominally similar federal grant, but will also need institutional agreement to accept the reduced rate, and securing that waiver is a step worth initiating early rather than at submission. At ten to fifteen awards across a 100,000 to 500,000 dollar range the total programme commitment is likely in the region of 3,000,000 to 5,000,000 dollars. The two-track structure means a small exploratory effort and a substantial method-development project compete in separate bands rather than against each other, which improves the odds for a focused twelve-month pilot. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Schmidt Sciences 2026 AI for Actionable Matter Modelling Request for Proposals for AI Methods Predicting Functional Properties of Catalysts, Semiconductors, Drugs, Batteries and Biomolecules is funded by Schmidt Sciences, AI for Science portfolio. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
Path to a Cure Research Grant is a grant from Cystic Fibrosis Foundation that funds basic science research aimed at restoring CFTR protein function or developing gene-based approaches to treat cystic fibrosis. The program specifically targets projects proposing restoration of CFTR protein function or approaches to fix or replace the defective CFTR gene. Awards provide up to $150,000 per year (plus 12% indirect costs) for up to two years, with grants competitively renewable for additional funding. Eligible applicants are independent investigators at for-profit and nonprofit institutions. Proposals must be hypothesis-driven with sufficient preliminary data. The fall cycle deadline is November 15, 2026.
Utilizing the Full Power of Empire AI is a Simons Foundation request for applications that funds basic science research making genuine use of multinode GPU clusters on New York State's Empire AI supercomputing consortium. The framing is deliberately specific: the Foundation is not funding AI applications generally but research that requires clusters of GPUs across multiple nodes, which excludes the large volume of scientific machine learning that runs comfortably on a single node and is therefore not compute-constrained. Target fields named in the call are computational astrophysics and physics, computational biology, mathematics, neuroscience and computational physical chemistry, together with foundational machine learning models, so the programme spans both AI-for-science and the development of foundation models themselves. The combination of a 6,000,000 dollar commitment across 6 to 12 awards, no published per-award ceiling, and an instruction that budgets should reflect the proposed research signals that the Foundation expects ambitious, compute-heavy proposals rather than incremental ones. The award period runs 1 June 2027 to 31 May 2029 against a 1 December 2026 deadline at 12 p.m. Eastern time. The binding constraint is institutional: eligibility is restricted to faculty at the nine Empire AI consortium member institutions, which makes this one of the most geographically concentrated significant AI funding opportunities currently open, and effectively a New York State research-capacity instrument rather than a national competition.
Schmidt Sciences opened a 'Scaling AI Safety for a Multi-Agent World' program with awards up to $1 million and an August 8, 2026 deadline. Against a backdrop of federal research slowdowns, here is why private science philanthropy matters more than ever, how these funders differ from federal agencies, and how researchers should approach them.
Read articleHAVI's 2026 round put $10 million into 19 projects selected from 501 proposals across 42 countries, bringing the two-year total past $21 million. The open-source release requirement and the development-award feeder tier tell you more about how to win than the acceptance rate does.
Read articleSchmidt Sciences' 2026 Science of Trustworthy AI RFP closes May 17 with two funding tiers — up to $1M (Tier 1) and $1–5M+ (Tier 2) over 1–3 years, with a 10% indirect cost cap. The three research aims target misalignment under distribution shift, predictive-validity evaluations, and oversight of superhuman systems. Here is why the structure favors team-based proposals.
Read article