Workshop: Data Driven and Computational Modeling of Materials Across Scales; Los Angeles, California; 10-12 May 2023

研讨会：跨尺度材料的数据驱动和计算建模；

• 批准号: 2325413
• 负责人: Amartya Banerjee
• 金额: $ 3.44万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325413&HistoricalAwards=false
• 关键词: Workshop; Data; Driven; Computational; Modeling

This award provides registration and travel support for 20 early career U.S. researchers to attend the Workshop on Data Driven and Computational Modeling of Materials Across Scales, which will be held 10-12 May 2023 in Los Angeles, California. The objective of this workshop is to bring together experts from a wide variety of technical backgrounds to present and deliberate on the topics of multiscale modeling of materials, computational mechanics, and data science/machine learning methods. The workshop will feature invited talks, poster presentations, lightning talks, panel discussions, and networking opportunities for researchers. The award will broaden the participation from graduate students, postdoctoral fellows, and junior faculty participants, particularly women and underrepresented minority researchers. In the selection process, consideration will be given to the inclusion of members of underrepresented groups, diversity of institutions that the participants represent, and the diversity of disciplines. A detailed workshop report, abstracts of all presentations, and video recordings of invited talks will be widely disseminated beyond the workshop.This workshop will engage researchers in the exchange of scientific ideas on current and future research directions in multiscale modeling of materials, computational mechanics, and data science/machine learning methods. The success of computational approaches to materials modeling hinges on having access to accurate, reliable and efficient simulation techniques across length and time scales. Most materials phenomena are multiscale in nature and properties of common engineering materials are often dictated by the properties of defects that interact at length scales much smaller than everyday macroscopic objects. As such, predictive computational tools suited to the length and time scales relevant to the problem physics, and techniques which span across scales are required. Data analytics and machine learning tools have provided many new and exciting avenues in computational materials research and computational mechanics. The best strategies for using these powerful new tools in multiscale materials modeling constitute an important and active area of ongoing research. The invited talks, poster presentations and panel discussions will inform the participants of the latest technical developments in the field and also provide the opportunity for experts to deliberate openly on challenges related to the aforementioned topics. These discussions are expected to eventually lead to new computational and data driven techniques for materials modeling, and to also drive the development of novel materials via bottom-up design strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项为20名美国早期职业研究人员提供注册和旅行支持，以参加将于2023年5月10日至12日在加州洛杉矶举行的跨尺度材料数据驱动和计算建模研讨会。本次研讨会的目的是汇集来自各种技术背景的专家，介绍和审议材料，计算力学和数据科学/机器学习方法的多尺度建模的主题。研讨会将邀请演讲，海报展示，闪电会谈，小组讨论和研究人员的网络机会。该奖项将扩大研究生，博士后研究员和初级教师参与者的参与，特别是女性和代表性不足的少数民族研究人员。在甄选过程中，将考虑到代表性不足群体的成员、参与者所代表的机构的多样性以及学科的多样性。详细的研讨会报告、所有演讲的摘要和受邀演讲的视频记录将在研讨会之外广泛传播。本次研讨会将邀请研究人员就材料、计算力学和数据科学/机器学习方法的多尺度建模的当前和未来研究方向交换科学思想。材料建模计算方法的成功取决于在整个长度和时间尺度上获得准确，可靠和有效的模拟技术。大多数材料现象在本质上是多尺度的，普通工程材料的性质通常由缺陷的性质决定，这些缺陷在比日常宏观物体小得多的长度尺度上相互作用。因此，预测计算工具，适合的长度和时间尺度相关的物理问题，和跨尺度的技术是必需的。数据分析和机器学习工具为计算材料研究和计算力学提供了许多新的和令人兴奋的途径。在多尺度材料建模中使用这些强大的新工具的最佳策略构成了正在进行的研究的重要和活跃的领域。特邀演讲、海报展示和小组讨论将向与会者介绍该领域的最新技术发展，并为专家们提供机会，公开讨论与上述主题有关的挑战。这些讨论有望最终导致新的计算和数据驱动的材料建模技术，并通过自下而上的设计策略推动新材料的开发。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。