NSF funds AI to revolutionize science & engineering
The U.S. National Science Foundation (NSF) is significantly increasing its investment in artificial intelligence, aiming to fundamentally reshape how scientific discovery and engineering innovation occur. Through a series of new programs, the agency is channeling resources into initiatives designed to accelerate the journey from groundbreaking ideas to tangible real-world applications, bolstering the nation’s competitive edge in the global AI landscape.
A cornerstone of this effort is the “Use-Inspired Acceleration of Protein Design” initiative, which is committing nearly $32 million to teams employing AI for the creation of enzymes, proteins, and materials with precisely engineered properties. This funding comes at a pivotal moment, as recent advances, notably tools like Google DeepMind’s AlphaFold, have dramatically improved the ability to predict protein structures. This shift has opened the door to designing these complex molecules for specific functions, a development that promises to revolutionize the creation of new materials, medicines, and industrial processes that once required years of development. Erwin Gianchandani, an NSF official, underscored the importance of this collaborative approach, stating that the NSF is “pleased to bring together experts from both industry and academia to confront and overcome barriers to the widespread adoption of AI-enabled protein design.” Projects currently underway include the development of AI-engineered enzymes for producing bio-based acrylates, used in products like paints and Plexiglas, and bacteria capable of creating recyclable, high-temperature-resistant plastics. These endeavors illustrate how AI-driven protein design could extend its impact across manufacturing, healthcare, and energy sectors.
While AI-driven design unlocks new scientific potential, the next crucial step is ensuring these innovations are robust enough for real-world deployment. To address this, the NSF is allocating over $2 million in planning grants for its “AI-Ready Test Beds” program. These test environments are designed to push AI systems to their operational limits by replicating the unpredictable, often chaotic conditions of the real world. This rigorous testing aims to validate and refine AI concepts, preparing them for practical use. Ellen Zegura, acting assistant director for CISE, highlighted the program’s unique leveraging of existing research facilities to drive AI progress, noting that upgrades will enable advanced AI evaluation, including how well systems adapt and recover from unexpected challenges. The test beds encompass a diverse range of settings, including a city-scale wireless network in New York, an agricultural systems lab at Cornell University, a disaster resilience hub at the University of Maryland, and autonomous vehicle testing grounds at the University of Michigan. Each location presents distinct real-world variables, offering AI systems the crucial opportunity to prove their reliability under dynamic conditions.
Beyond design and real-world validation, the NSF recognizes the need for accessible tools that enable researchers to rapidly translate promising ideas into fully tested solutions, regardless of their physical location. To this end, the agency is establishing a new network of programmable cloud laboratories. These facilities will feature AI-enabled systems capable of managing every stage of an experiment, from initial setup to interpreting results. Researchers will be able to log in remotely, design experimental workflows, and observe them unfold in real time, with the system dynamically adjusting conditions as new data emerges. While the initial focus areas will be biotechnology and materials science, the broader vision is to democratize access to cutting-edge experimentation, opening opportunities for universities, startups, and even educational institutions to participate in high-level research. This expanded access could significantly accelerate AI-enabled science across a wider community, particularly in areas like AI-guided drug discovery, where companies are already moving computer-designed drugs into clinical trials.
The overarching aim behind these interconnected programs is to empower researchers, granting them the freedom to conceptualize bolder ideas, execute them more swiftly, and tackle problems that were once considered insurmountable. By strategically linking advanced AI capabilities with the necessary tools and environments for their application, the NSF is actively ensuring that scientific breakthroughs originating in the laboratory can translate into substantial benefits for industries and individuals nationwide.