Conference: Materials Genome Initiative (MGI) Biennial Principal Investigator Workshop; Washington, DC; July 30-31, 2024
Conference: Materials Genome Initiative (MGI) Biennial Principal Investigator Workshop; Washington, DC; July 30-31, 2024
批准号:
2422384
负责人:
Thao Nguyen
金额:
$15.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
已结题
起止时间:
2024-04-15 至 2024-11-30
中文摘要
该奖项资助了一个研讨会,让来自国家科学基金会(NSF)、能源部(DoE)、空军研究实验室(AFRL)、海军研究办公室(ONR)、美国陆军作战能力发展司令部(DEVCOM)和国家标准与技术研究所(NIST)以及其他各种联邦机构的材料基因组倡议(MGI)受赠者和项目经理聚集在一起。MGI于2011年推出,旨在通过结合实验利用数据和计算工具的力量,以极小的成本加快新材料的发现、设计、开发和部署。作为NSF对MGI的回应,设计材料以革命和设计我们的未来(DMREF)计划寻求促进在合成、实验、理论、计算和数据科学专业知识的材料研究人员之间在高度迭代的反馈循环上的合作,在高度迭代的反馈循环中,实验结果直接为理论和计算提供信息,反之亦然。该研讨会将DMREF获奖者与其他联邦机构支持的获奖者聚集在一起,以讨论他们的挑战和成功;提供对该倡议当前状况的评估;并成为利用数据、数据驱动的AI/ML和社区范围基础设施发展的快速增长进行协作的跳板。这次研讨会的一个主要目标是评估各参与机构对MGI的现状和机会。追求以下目标:1)通过人工智能、机器学习和自然语言处理等数据驱动的方法促进材料研究,2)沿着材料发展连续体过渡基础研究,3)解决教育和多样性问题,以及4)促进MGI PIs之间的合作。研讨会将包括不同联邦机构与MGI相关活动的概述演讲、与会者的技术演示、海报会议和关于总体主题的分组会议。主要主题将在一份报告中提出,以便向更广泛的材料研究界和公众传播。该计划包括专注于劳动力发展的工作组,鼓励学生和博士后学者参加和参与。讲习班的结果将以电子(PDF)会议议程小册子的形式提供给更广泛的材料界和公众,其中将包括海报和演示文稿的摘要。研讨会将于2024年7月30日至31日在华盛顿市中心举行,此前举行了一系列成功的研讨会,最近一次是在2022年6月举行的。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
This award funds a workshop to bring together Materials Genome Initiative (MGI) grantees and program managers from the National Science Foundation (NSF), the Department of Energy (DoE), the Air Force Research Laboratory (AFRL), the Office of Naval Research (ONR), the US Army Combat Capabilities Development Command (DEVCOM), and the National Institute of Standards and Technology (NIST), and various other federal agencies with a stake in the MGI. The MGI was launched in 2011 to accelerate the discovery, design, development, and deployment of new materials, at a fraction of the cost, by harnessing the power of data and computational tools in concert with experiment. As NSF's response to the MGI, the Designing Materials to Revolutionize and Engineer our Future (DMREF) program seeks to foster collaborations between materials researchers with expertise in synthesis, experiment, theory, computation, and data science on highly iterative feedback loops in which experimental results directly inform theory and computation, and vice versa. This workshop brings together DMREF awardees with those supported by other federal agencies in order to discuss their challenges and successes; provide an assessment of the current status of the Initiative; and be a springboard to collaborative work leveraging rapid growth in data, data-driven AI/ML, and community-wide infrastructure development. A primary goal of this workshop is to assess the current status and opportunities for MGI across participating agencies. The following aims are pursued: 1) Accelerate materials research through data-driven approaches including artificial intelligence, machine learning, and natural language processing, 2) Transition fundamental research along the Materials Development Continuum, 3) Address educational and diversity issues, and 4) Promote collaboration among MGI PIs. The workshop will consist of overview talks of MGI-related activities at various federal agencies, technical presentations by the attendees, poster sessions, and breakout sessions on overarching topics. The overarching themes will be presented in a report for dissemination to the broader materials research community and the public. The program includes workgroups focused on workforce development where students and postdoctoral scholars are encouraged to attend and participate. Results from the workshop will be made available to the broader materials community and the public in the form of an electronic (PDF) Conference Proceeding booklet that will include abstracts from the posters and presentations. The workshop, which will be held in downtown Washington DC on July 30-31, 2024, follows a series of successful workshops, the latest being held in June 2022.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.
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