NSF AI grant $1.4M: a GPU supercomputer for quantum- and AI-driven chemistry and materials research (University of New Mexico, MRI)
The NSF awarded about $1.4M (Major Research Instrumentation) to acquire and operate a large-memory GPU parallel supercomputing cluster for AI- and quantum-driven chemistry and materials research at the University of New Mexico (UNM). It supports computational design of new materials/molecules/reactions and new quantum-computing algorithms for many-body systems.
Grant overview (primary data)
- Award amount$1,399,999
- RecipientUniversity of New Mexico（NM）
- ProgramMajor Research Instrumentation
- Period2026-03-15 〜 2029-02-28
- FunderU.S. National Science Foundation (NSF) / NSF
Key points
- Acquires a large-memory GPU parallel supercomputer for AI- and quantum-driven chemistry/materials (MRI)
- Shared by UNM's Center for Computational Chemistry (C^3) and quantum information center
- Supports computational design of materials/molecules/reactions and quantum-computing algorithms
- Trains interdisciplinary researchers via NRT graduate and undergraduate summer programs
- About $1.4M; University of New Mexico; MRI
The NSF awarded about $1,399,999 to the University of New Mexico (UNM) as a Major Research Instrumentation (MRI) grant to acquire a GPU supercomputer for quantum- and AI-driven chemistry and materials research (NSF Award 2511915).
Per the abstract, the project supports acquisition and operation of a dedicated GPU-based, large-memory, parallel high-performance supercomputing cluster as a state-of-the-art resource for AI- and quantum-driven chemistry and materials research at UNM. The resource enables simulations and algorithm development by faculty and researchers from chemistry, physics, and engineering, affiliated through the Center for Computational Chemistry (C^3) and the Center for Quantum Information and Control (an NSF Focused Research Hub in Theoretical Physics).
The system advances understanding of the complex quantum phenomena underlying the computational design of new chemicals, biomolecules, reactions, and materials, and the development of novel quantum-computing algorithms for simulating complex many-body systems. Broader impact comes through education and training of the next generation of interdisciplinary chemistry, chemical-biology, and materials researchers via the NSF Research Traineeship (NRT) Quantum Photonics and Quantum Technology (QPAQT) graduate program, the Quantum Undergraduate Research Experience (QU-REACH) summer program, and new computationally driven courses.
Why it matters
A case of building research GPU infrastructure for AI and quantum computing at a regional university. For those tracking AI × quantum, computational chemistry, and materials design compute, a useful read on U.S. research-infrastructure investment.
FAQ
Why do chemistry/materials need GPUs?
How does quantum computing fit in?
Sources (primary)
Source: NSF Award Search (U.S. National Science Foundation, public domain). Amounts are the obligated amount. For privacy, we do not handle principal investigator names.
- NSF Award (original, official)
- NSF Award ID: 2511915