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  • 【SLAI Seminar】30th:AI for Science: From Infrastructure Development to Agentic Science at Scale (March 24 , 10:00)

【SLAI Seminar】30th:AI for Science: From Infrastructure Development to Agentic Science at Scale (March 24 , 10:00)

March 24, 2026 Forum Schedule

SLAI Seminar 31st Session will be discussing the topic on "AI for Science: From Infrastructure Development to Agentic Science at Scale? ",  from 10am-11:30am, March 24th (Tuesday) at Room B401, online participation is welcome.

 (Tencent Meeting ID: 646-592-351)

 

About the Speaker:

 

Linfeng Zhang is the Chairman of the AI for Science Institute, Beijing, as well as the founder and Chief Scientist of DP Technology. He holds a Bachelor of Science degree from Peking University and a Ph.D. in Applied Mathematics from Princeton University. Linfeng has long been dedicated to interdisciplinary research in AI for Science, achieving significant results in machine learning, computational physical chemistry, materials, and drug design. He has been consistently listed in Stanford University's "World's Top 2% Scientists" ranking for multiple years. As a core developer, Linfeng has led the creation of a series of micro-scale simulation algorithms and corresponding open-source software. In 2020, DeePMD won the ACM Gordon Bell Prize, the highest honor in high-performance computing, and was also selected as one of China's Top 10 Scientific Breakthroughs of 2020 by academicians of the Chinese Academy of Sciences and Engineering.

 

 

Abstract:

In this talk, I will first introduce the explorations and practices of DP Technology and its ecosystem partners in building and implementing the infrastructure for AI for Science. Here, “infrastructure” denotes principle- and data-based modeling systems, knowledge databases replacing traditional literature, intelligent experimental platforms, and integrated computing frameworks. Furthermore, I will discuss how the integration of AI with these foundational components is reshaping the paradigms of scientific research and industrial R&D processes. In particular, I will explore the potential emergence of a new paradigm: agentic science at scale.

 

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