NAIRR Science Program Reshapes Scientific Research, Powered by NVIDIA AI Infrastructure

National Science Foundation's NAIRR pilot program has driven innovative research across the U. for over 700 projects - spanning protein prediction and infectious disease outbreak management. For the past two years, t he U.

NVIDIA also provided technical support to onboard and assist the researchers throughout their projects. With NVIDIA's AI infrastructure support and DGX reference architecture providing dedicated resources, researchers have collapsed workflow timelines and uncovered groundbreaking technologies that will reshape and advance industries such as healthcare, agriculture and energy. The potential for scientific exploration and discovery across the nation through NAIRR is boundless.

Learn more about a few NAIRR projects below. Physical Simulations With Polymathic AI's Well Dataset Simulation-to-real pipelines are becoming increasingly common across industries as a safer, more cost-efficient deployment method. Polymathic AI - a coalition of international scientists from Flatiron Institute, Cambridge University and Lawrence Berkeley National Lab - with the help of NVIDIA GPUs and NVIDIA NVLink interconnect technology , is strengthening physical, fluidlike simulations with its large-scale dataset called the "Well.

" The dataset will be used to train the largest and most broadly applicable foundation model for fluidlike behavior to date. This foundation model, named Walrus , has been made publicly available along with its data, code and pertained weights. Polymathic AI's approach builds on previous work in physics pretraining environments - addressing current limitations in scale and pretraining diversity.

The research group also plans to explore scaling laws to help accelerate the development of more powerful foundation models for scientific applications. University of Michigan 's Fusion Model for Energy Storage Energy, a foundation of society, requires designing novel and efficient materials for energy storage and conversion. Researchers at the University of Michigan, led by Professor Venkat Viswanathan in the Department of Aerospace engineering, are developing a model-fusion framework that brings together domain-specific molecular AI and general-purpose large language models.

The goal is to help computational scientists more easily explore chemical space, ask chemistry-specific questions in natural language and identify promising materials for next-generation energy technologies. The family of molecular foundation models, MIST (the Molecular Insight SMILES Transformers), is designed for discovery and exploration across chemical space. MIST models were pretrained on large unlabeled molecular datasets and use a novel tokenizer, Smirk, to better capture nuclear, electronic, geometric, isotopic and stereochemical information from molecular representations.

MIST models have been fine-tuned on more than 400 structure-property relationships and can match or exceed state-of-the-art performance across benchmarks spanning electrochemistry, quantum chemistry, physiology and other domains. MIST was developed on a 40-GPU NVIDIA DGX cluster the researchers gained as part of a NAIRR allocation and an additional 200,000 NVIDIA GPU hours on ALCF's Polaris cluster. The team used NVIDIA's NGC PyTorch container to support reproducible GPU-accelerated development across the different clusters.