The U.S. National Science Foundation's NAIRR pilot program has supported more than 700 research projects across the United States over the past two years, with NVIDIA providing dedicated AI infrastructure that has accelerated work in protein prediction, infectious disease management, and other fields. The program gives scientists access to powerful computing resources, collapsing workflow timelines and enabling advances that could reshape healthcare, agriculture, and energy.
NVIDIA contributed a cloud-based resource offering researchers dedicated access to at least four NVIDIA DGX nodes for a minimum of one month, along with technical support. The DGX reference architecture provided consistent, high-performance computing that helped teams move faster.
Physical simulations with Polymathic AI's Well dataset
Polymathic AI, a coalition of scientists from Flatiron Institute, Cambridge University, and Lawrence Berkeley National Lab, is strengthening physical simulations with a large-scale dataset called the Well. The dataset will train Walrus, the largest and most broadly applicable foundation model for fluidlike behavior to date. The model, along with its data, code, and pretrained weights, is publicly available. The group also plans to explore scaling laws to accelerate development of more advanced foundation models for scientific applications.
University of Michigan's fusion model for energy storage
Researchers at the University of Michigan, led by Professor Venkat Viswanathan, are developing a model-fusion framework that combines domain-specific molecular AI with general-purpose large language models. The family of molecular foundation models, MIST (Molecular Insight SMILES Transformers), was pretrained on large unlabeled molecular datasets and uses a novel tokenizer, Smirk, to capture detailed molecular information. MIST models were fine-tuned on more than 400 structure-property relationships and can match or exceed state-of-the-art performance across electrochemistry, quantum chemistry, and physiology benchmarks.
The team developed MIST on a 40-GPU NVIDIA DGX cluster from NAIRR and an additional 200,000 GPU hours on ALCF's Polaris cluster, using NVIDIA's NGC PyTorch container for reproducible development. Fusing MIST with LLMs makes accurate quantum-chemical calculations more accessible and accelerates the design of energy storage systems needed for electrification of heavy-duty and aviation transport.
Boston University's BEACON AI pipeline for infectious disease detection
Boston University's Hariri Institute for Computing and the Center on Emerging Infectious Diseases is training a large language model on a corpus of infectious disease documents to support the BEACON outbreak monitoring program. The model analyzes online posts about emerging disease outbreaks globally, extracting features for categorization and prioritization. BEACON processes signals from HealthMap, news, social media, and expert communications to generate concise outbreak reports. These reports inform clinical guidelines and identify data gaps.
Ioannis Paschalidis, director of the Hariri Institute, said, "When you talk to infectious disease experts about what they used to do before we developed this pipeline, it used to take several hours for them to compose a report. Now, producing a report gets done in roughly two minutes."
Other universities, including Harvard, Stanford, and Colorado State University, are also using NAIRR and NVIDIA resources for scientific breakthroughs. These projects are just a few examples of the Research enabled by NAIRR.
Why this matters for Science and Research professionals
The NAIRR program demonstrates that access to dedicated AI infrastructure can drastically reduce the time needed for computation-heavy tasks. The BEACON pipeline's two-minute report generation, down from hours, is a concrete example. Such efficiency gains allow scientists to focus on interpretation and discovery. As AI becomes more embedded in scientific workflows, familiarity with these tools and the ability to secure compute resources will be critical. The program also highlights the increasing importance of AI for Science & Research in accelerating discovery.
Learn more about the NAIRR pilot program and explore how NVIDIA is driving academic research.
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