美国田纳西大学Sergei V. Kalinin教授学术报告 | JMI第十一期线上学术研讨会预告

9月6日Journal of Materials Informatics 将迎来第十一期系列学术研讨会，本期研讨会有幸邀请到了美国田纳西大学Sergei V. Kalinin教授作为报告人，报告题目为：AI Scientist: Automating Electron and Scanning Probe Microscopes for Physics Discovery and Materials Optimization。Kalinin教授在机器学习与人工智能应用于材料科学研究的领域里具有深远的影响力，他曾荣获多个顶级奖项，包括布拉瓦特尼克物理科学奖（2018）和总统青年科学家和工程师奖（2009)；并领导了多个重大项目，包括美国能源部首个将机器学习与物理科学相结合的项目：Institute for Functional Imaging of Materials (IFIM 2014-2019)；在田纳西大学，他还参与了全国首批机器学习驱动的材料探索工作的建设。

本次线上研讨会将于北京时间2024年9月6日上午9：00在ZOOM会议室进行，由上海大学刘轶教授担任主持人。本次会议全程免费，JMI编辑部欢迎广大学者专家积极参与，共享领域内前沿学术成果，届时会有发言提问环节，欢迎各位学者专家积极参会，交流讨论。

简介：

主题：AI Scientist: Automating Electron and Scanning Probe Microscopes for Physics Discovery and Materials Optimization

会议时间：

9:00 AM, 6 September, 2024 (Beijing)

8:00 PM, 5 September, 2024 (GMT-5)

ZOOM会议号：824 6405 0616

Making microscopes automated and autonomous is a North Star goal for areas ranging from physics and chemistry to biology and materials science – with the dream applications of discovering structure-property relationships, exploring physics of nanoscale systems, and building matter on nanometer and atomic scales. Over the last several years, increasing attention has been attracted to the use of AI interacting with physical system as a part of active learning – including materials discovery and optimization, chemical synthesis, and physical measurements. For these active learning problems, microscopy arguably represents an ideal model application combining aspects of materials discovery via observation and spectroscopy, physical learning with relatively shallow priors and small number of exogenous variables, and synthesis via controlled interventions. I introduce the concept of the reward-driven experimental workflow planning and discuss how these workflows can be implemented via domain-specific hyper languages. The applications of classical deep learning methods in streaming image analysis are strongly affected by the out of distribution drift effects, and the approaches to minimize though are discussed. The real-time image analysis allows spectroscopic experiments at the predefined features of interest and atomic manipulation and modification with preset policies. I further illustrate ML methods for autonomous discovery, where the microstructural elements maximizing physical response of interest are discovered. Complementarily, I illustrate the development of the autonomous physical discovery in microscopy via the combination of the structured Gaussian process and reinforcement learning, the approach we refer to as hypothesis learning. Here, this approach is used to learn the domain growth laws on a fully autonomous microscope. The future potential of Bayesian active learning for autonomous microscopes is discussed. These concepts and methods can be extended from microscopy to other areas of automated experiment.

本会议将通过Journal of Materials Informatics 期刊官方微信视频号、科研云以及蔻享学术平台进行同步直播。

Journal of Materials Informatics（Online ISSN: 2770-372X）是聚焦材料信息学领域的国际英文学术期刊。于2021年4月由OAE Publishing Inc. 正式创刊，由中国科学院院士、香港工程科学院院士、上海大学材料基因研究院院长张统一教授担任创刊主编，由俄罗斯自然科学院外籍院士、哈尔滨工业大学（深圳）材料基因与大数据研究院院长刘兴军教授担任执行主编。期刊目前共有42位编委，包含国内外院士10名；以及56位青年编委。期刊已被ESCI, CAS, CNKI, Dimensions, ResearchGate, Lens, J-Gate收录，将在2025年6月获得首个影响因子。

期刊旨在通过紧密集成和智能化的方式将理论、实验、计算和人工智能协同结合，以推进和加速材料发现、设计和部署的步伐。期刊为研究人员提供了一个展示、发表和交流材料信息学相关研究的平台，寻求打破材料科学与工程、数据科学与工程及人工智能之间的壁垒。欢迎领域学者的来稿！

期刊主页：https://www.oaepublish.com/jmi

投稿地址：https://oaemesas.com/login?JournalId=jmi

联系编辑：editorialoffice@jmijournal.com