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)

报告主题：科学智能：从基础设施建设到规模化智能科学

时间：2026年3月24日（周二）上午10:00-11:30

地点: 深圳河套学院B401教室

线上参与：腾讯会议号646-592-351

主讲嘉宾：张林峰博士

主持人：姬艳丽教授

讲者简介About the Speaker:

张林峰是北京科学智能研究院院长，同时是深势科技创始人兼首席科学家。张博士本科毕业于北京大学，并在普林斯顿大学获得应用数学博士学位，长期致力于科学智能领域的交叉研究，在机器学习、计算物理化学、材料和药物设计等领域取得显著成果，连续多年入选斯坦福大学"全球前2%顶尖科学家"榜单。作为核心开发者，张博士主导开发了一系列微尺度模拟算法及相应的开源软件。2020年，其成果DeePMD荣获高性能计算领域最高荣誉——ACM戈登贝尔奖，同时被中国科学院院士和中国工程院院士评选为"2020年中国十大科技进展"之一。

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.

报告摘要：

本次报告中，张博士将首先介绍深势科技及其生态合作伙伴在构建和部署科学智能基础设施方面的探索与实践。报告所述的"基础设施"，具体指的是基于原理和数据的建模系统、替代传统文献的知识数据库、智能化实验平台，以及一体化计算框架。随后，张博士将探讨人工智能如何与这些基础要素深度融合，正在重塑科学研究和产业研发的范式。特别值得关注的是，张博士将深入分析一种新范式——规模化智能科学的潜在发展趋势。

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.