{"id":349,"date":"2023-01-13T12:31:12","date_gmt":"2023-01-13T17:31:12","guid":{"rendered":"https:\/\/research-multi-dev.utk.edu\/energy-dev\/?page_id=349"},"modified":"2026-04-14T16:04:13","modified_gmt":"2026-04-14T20:04:13","slug":"ai-for-knowledge-and-discovery","status":"publish","type":"page","link":"https:\/\/research.utk.edu\/aitn\/research-areas\/ai-for-knowledge-and-discovery\/","title":{"rendered":"AI For Knowledge and Discovery"},"content":{"rendered":"\n
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ai for knowledge and discovery<\/h1>\n\n\n\n

Pioneering the future of scientific and engineering progress<\/em><\/p>\n<\/div>

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UT researchers are using AI to advance science and engineering, collaborating across domains to develop and apply AI systems<\/a> that are transforming how we work by increasing speed and removing bottlenecks. They also work to advance AI models by training them in the physical laws that govern our world.<\/p>\n\n\n\n

AI systems including machine learning\u2013enabled microscopy, real-time analysis via neural networks, and simulations enable rapid discovery of new physical laws, high-performing materials, and chemical properties. These discoveries generate advancements in fields ranging from medicine and smart manufacturing to hypersonics and nuclear energy.<\/p>\n<\/div>\n\n\n\n

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UT’s Approach<\/h2>\n\n\n\n

UT researchers are transforming the way knowledge is created and applied by deploying AI across every stage of discovery. They are building self-driving laboratories that integrate machine learning with automated experimentation, accelerating the pace of science and enabling real-time insights. Physics-augmented AI approaches refine digital twins, discover new quantum materials, and extend the limits of simulation and modeling.<\/p>\n\n\n\n

Faculty at UT are applying hybrid quantum\u2013machine learning models and large language frameworks to chemical and molecular discovery, forging new pathways in polymers, drug design, and complex molecular systems. At the systems scale, UT teams harness AI to model fusion reactors, evaluate thousands of nuclear designs, and shape the next generation of hypersonics and intelligent manufacturing. Anchored by the National Science Foundation Materials Research Science and Engineering Center and the university\u2019s science-informed AI faculty cluster, these efforts are expanding the frontiers of knowledge and redefining what is possible. <\/p>\n\n\n\n

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With AI, now we can accelerate discoveries in the rules of chemistry. By harnessing AI tools and large databases, we can systematically screen and examine not just dozens or hundreds but even millions of molecules, reactions, and materials. We\u2019re a university, and we\u2019re a state, that can convert basic science into world-changing, society-changing projects.\u201d<\/h2>\n\n\n\n
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Konstantinos Vogiatzis, Associate Professor of Chemistry<\/em><\/p>\n<\/blockquote>\n<\/div>\n<\/div>\n\n\n\n

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Highlights<\/h2>\n\n\n\n
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UT Awarded NSF Materials Research Science and Engineering Center<\/a><\/h3>\n\n\n\n

This prestigious center has been awarded $18 million to develop AI and computational tools and deploy them in the design and synthesis of next-generation materials in two areas: quantum materials and materials for extreme environments.  

Learn more about this research.<\/a>

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Yao Receives $1.1 Million to Create Algorithm that Personalizes Heart Surgery<\/a><\/h3>\n\n\n\n

Bing Yao, an expert on physics-informed machine learning, is co-leading a four-year project to optimize cardiac surgical planning by constructing a physics-based model of the cardiac system and training a deep-learning AI on CT, MRI, and other relevant data types. The project is supported by the National Science Foundation and the National Institutes of Health.

Learn about this research.<\/a>
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Ahmadi Coauthors Two Perovskite Breakthroughs Published in Nature<\/a><\/h3>\n\n\n\n

Mahshid Ahmadi specializes in integrating machine learning and automated processes to accelerate materials discovery and synthesis. She and her lab have contributed to international breakthroughs in creating perovskites\u2014high-potential materials for next-generation solar panels, LEDs, and lasers.

Learn about her work.<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Zhang to Explore Novel Property of Laser-Induced Plasmas with NSF ECLIPSE Grant<\/a><\/h3>\n\n\n\n

Zhili Zhang received a three-year grant from the National Science Foundation\u2019s ECLIPSE program to investigate how different laser parameters impact plasma filaments. The project includes creating a physics-informed neural network to make plasma kinetic models, which Zhang will validate experimentally.

Learn more about this research.<\/a>

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Facilities & Initiatives<\/h2>\n\n\n\n

Faculty study and build AI systems in state-of-the-art facilities on and off campus. Centers and research groups receive support from and partner with organizations including the National Science Foundation, the US Department of Energy, and industry leaders.<\/p>\n\n\n\n