NSF–NVIDIA Open AI Models to Transform U.S. Scientific Research
On August 14, 2025, the U.S. National Science Foundation (NSF) announced an ambitious partnership with NVIDIA aimed at creating fully open, multimodal AI models to make scientific research faster, more transparent, and more competitive. The initiative will be led by the Allen Institute for AI (Ai2) and is named Open Multimodal AI Infrastructure to Accelerate Science (OMAI). Under the program, NSF will contribute $75 million and NVIDIA $77 million — a total package of $152 million — to build a comprehensive open science AI ecosystem, including hardware, software, training, documentation, and operations.
What is OMAI
OMAI is essentially a national AI infrastructure built in the public interest, designed to train large multimodal (text, image, scientific data, etc.) models aligned with scientific research. Its most distinctive feature is being “fully open”—meaning model weights, training data (where legally and ethically possible), research code, benchmarks, and documentation will be widely available to the research community. The goal is clear: to accelerate discovery, strengthen reproducibility, and ensure that cutting-edge AI is accessible to universities, government labs, and non-profits without barriers.
NVIDIA will contribute its state-of-the-art AI computing stack—expected to include Blackwell-generation high-performance GPU systems, optimized AI software suites, and engineering support to enable large-scale training and efficient inference. Meanwhile, Ai2 will organize model development, data preparation, benchmarking, release protocols, and researcher-facing tools, ensuring smooth adoption across the open science community.
NSF’s Priority
NSF has made it clear that AI is no longer just a supportive tool but is moving to the very center of the scientific process. Acting Director Brian Stone called the partnership a “strategic move to safeguard and strengthen America’s leadership in science and technology.” The vision is that researchers—even from small departments or rural campuses—will gain access to the same high-quality AI models and training toolkits usually reserved for big tech companies, ensuring that breakthroughs are not limited to a handful of large institutions.
Early Focus: New Materials, Biomedical Science, and Overcoming LLM Weaknesses
In its first phase, OMAI will focus research efforts on three major fronts:
- Discovery of New Materials: Using AI for advanced simulations, property prediction, and design optimization, with applications expected in energy storage, semiconductors, and sustainable manufacturing.
- Biomedical Advancements: Deploying multimodal models for protein function prediction, biological system modeling, and drug discovery, thereby shortening timelines for understanding diseases and developing treatments.
- Addressing Large Language Model (LLM) Limitations: Developing science-focused LLMs with greater accuracy, stability, source transparency, and tool usability (e.g., coding, database queries, lab automation). The aim is to make LLM-assisted research more trustworthy.
Alignment with the White House AI Action Plan
This investment aligns directly with the White House’s “Winning the Race: America’s AI Action Plan”, introduced as a federal strategy in July–August 2025. The plan prioritises accelerating AI innovation, expanding U.S. AI infrastructure, and strengthening leadership in international AI diplomacy and security. OMAI embodies these objectives by combining public-interest AI, open models, and high-quality compute access. The government has underscored that if the U.S. secures decisive leadership in AI, its impact could rival the Industrial and Information revolutions—driving breakthroughs in energy, medicine, education, media, and core scientific disciplines.
Strong Warning from LLM Vulnerabilities
Demonstrations at Black Hat USA 2025 showed that LLM-integrated applications can pose real-world risks—for example, manipulating a generative AI agent through a “poisoned” calendar invite to control smart-home devices. This was not just a flashy hack but a fundamental design lesson: wherever LLMs have tool-invocation powers—emails, calendars, IoT, code execution, cloud APIs—strict governance, permissions, audit-logging, and defences against indirect prompt injection are mandatory.
For this reason, initiatives like OMAI will embed security as a core design principle: model cards/system cards, red-teaming reports, evaluations, data-lineage documentation, and responsible release protocols will ensure transparency and community scrutiny, enabling vulnerabilities to be caught early and strengthening the ecosystem overall. One of open science’s greatest advantages is a faster feedback loop for security.
WSU–Sweden Student Exchange for AI Security
NSF recently awarded nearly $450,000 to Washington State University (WSU) to fund an exchange program with Linköping University in Sweden, aimed at training students in AI cybersecurity research. The program will focus on privacy-preserving machine learning, secure AI tool integration, and the protection of smart city, healthcare, and autonomous systems. This reflects the U.S. strategy of balancing open AI research with strong security priorities and international cooperation—emphasising both talent development and knowledge-sharing.
How Research Will Change
Today, the cost and compute requirements of developing large AI models put them out of reach for most academic institutions, concentrating capabilities in the hands of a few corporations. OMAI seeks to break this imbalance by making a national-level, open, high-performance AI stack available to the research community. This will ensure:
- Improved reproducibility of experiments, with transparent model/data/code versions.
- Faster development of domain-specific models (materials, biology, climate) through collaborative contributions on a shared open baseline.
- Better education and skill development, as students gain hands-on experience with real, large-scale models.
- Stronger accountability for policymakers, through evaluations and benchmarks openly available for impact assessment.
The Road Ahead
The roadmap will deliver phased outputs, including open-weight model families, training cookbooks, data curation guidelines, red-teaming/security evaluations, and researcher-facing tools. Early focus will be on public-interest datasets, scientific literature, structured knowledge graphs, and lab/simulation-generated data. With NVIDIA’s infrastructure, large-scale pre-training runs, research on scaling laws, and efficient fine-tuning/adaptation will accelerate significantly.
Community participation will be central—researchers, educators, and students alike will have opportunities to contribute through bug reports, security findings, fair-use data mapping, domain evaluations, and use-case guides. This collective effort is the true power of “open”—hundreds of institutions contributing intelligence and labour to a shared platform.
Conclusion
The NSF–NVIDIA partnership is more than just a technical announcement; it is a roadmap for the next decade of research. Under Ai2’s leadership, OMAI will adapt open models to scientific needs and strengthen America’s AI-driven research capacity at an institutional level. Lessons from Black Hat 2025—tool-invocation governance, defences against indirect prompt injection, and transparent evaluations—must remain integral to OMAI’s design. International collaborations, such as the WSU–Sweden student exchange, further show that the U.S. aims to lead this race not in isolation but through partnerships, standards, and open science.
As a result, from new materials and medicines to energy, education, and communication, this initiative opens the door to faster, more competitive, and secure AI-powered progress across multiple branches of science.
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