Collaborative Research: CyberTraining: Implementation: Medium: The Informatics Skunkworks Program for Undergraduate Research at the Interface of Data Science and Materials Science

合作研究：网络培训：实施：媒介：数据科学和材料科学接口本科生研究信息学 Skunkworks 计划

• 批准号: 2016981
• 负责人: Mahmood Mamivand
• 金额: $ 15.2万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016981&HistoricalAwards=false
• 关键词: Collaborative; Research; CyberTraining; Implementation; Medium

The project will develop a sustainable and scalable approach to train a workforce skilled in research and application of machine learning (ML) in materials informatics. ML in materials informatics is rapidly transforming materials science and engineering (MS&E) by an unprecedented ability to extend materials databases, improve materials simulation, mine texts, automate materials research and development, and accelerate materials design. As pointed out by multiple recent studies, including from the National Academies and the Minerals, Metals & Materials Society (TMS), it is essential to train a next generation workforce in ML for materials informatics to realize its enormous potential for improving the human condition through advanced materials. Unfortunately, ML in materials informatics is almost completely absent from today's materials curricula at the undergraduate level. However, the power of informatics tools combined with their rapid evolution and relative novelty in MS&E creates an opportunity for engaging undergraduates (UGs) with active learning through impactful research. With this motivation, the project will create new infrastructure and an ecosystem for the engagement and training of UGs across the U.S. in research using applied ML in materials informatics, called the Informatics Skunkworks. The Skunkworks consists of mentor/UG teams performing research in materials informatics. The project provides the teams with new resources, consisting of curricula, software, and research problems, and with a community of practice to support research and to work effectively and collaboratively. The low cost and accessibility of ML and materials informatics creates an opportunity for Skunkworks to engage mentors and students with limited research resources, particularly at institutions serving underrepresented groups. The Skunkworks is a sustainable and scalable approach that can fulfill this unmet need by training a diverse workforce skilled in research and application of ML for materials informatics. The project will provide freely available (a) curriculum to train UGs in relevant ML, materials informatics and research professional development, (b) software tools that augment existing ML packages to be UG accessible, and (c) authentic and appropriate-level research problems. The proposed work will also develop a community of practice to enable a network of productive mentor/UG research teams to effectively and collaboratively use the curricular, software and other resources developed by the project to support transforming the future workforce. The intellectual merit of the proposed work is to (a) develop scalable resources to increase UG experience and learning in research at the boundary of data science and materials science and engineering, (b) grow a community of mentors and UG researchers engaged in materials informatics research, and (c) increase the utilization of data science tools for solving critical problems in MS&E and related fields through workforce development and materials informatics training. The broader impact of the proposed work is to (a) freely disseminate enabling curricula and tools for materials informatics, (b) train staff, mentors and UGs in broadly applicable research and professional skills, and (c) develop a diverse community of practice for materials informatics researchers. The project will enable the development of a new workforce capable of advanced materials informatics, especially for underrepresented groups, by supporting primarily UG institutions and community colleges that often have limited research resources. This project is funded by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering, with the Division of Materials Research in the Directorate for Mathematical and Physical Sciences also contributing funds.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.

该项目将开发一种可持续且可扩展的方法来培训熟练从事材料信息学机器学习（ML）研究和应用的劳动力。材料信息学中的机器学习正在迅速改变材料科学与工程 (MS&E)，它具有前所未有的扩展材料数据库、改进材料模拟、挖掘文本、自动化材料研究和开发以及加速材料设计的能力。正如包括美国国家科学院和矿物、金属与材料学会 (TMS) 在内的多项近期研究指出的那样，有必要培训下一代材料信息学机器学习人员，以实现其通过以下方式改善人类状况的巨大潜力：先进材料。不幸的是，材料信息学中的机器学习几乎完全没有出现在当今本科阶段的材料课程中。然而，信息学工具的力量加上其快速发展和 MS&E 领域的相对新颖性，为本科生 (UG) 通过有影响力的研究积极学习创造了机会。出于这一动机，该项目将创建新的基础设施和生态系统，以便美国各地的 UG 参与和培训材料信息学中应用机器学习的研究，称为信息学 Skunkworks。 Skunkworks 由从事材料信息学研究的导师/UG 团队组成。该项目为团队提供了新的资源，包括课程、软件和研究问题，以及支持研究和有效协作的实践社区。机器学习和材料信息学的低成本和可访问性为 Skunkworks 创造了一个机会，吸引研究资源有限的导师和学生，特别是在服务于代表性不足群体的机构中。 Skunkworks 是一种可持续且可扩展的方法，可以通过培训擅长材料信息学机器学习研究和应用的多元化劳动力来满足这一未满足的需求。 该项目将提供免费的 (a) 课程来培训 UG 相关 ML、材料信息学和研究专业发展，(b) 增强现有 ML 包以方便 UG 访问的软件工具，以及 (c) 真实且适当级别的研究问题。拟议的工作还将开发一个实践社区，使富有成效的导师/UG研究团队网络能够有效地协作使用该项目开发的课程、软件和其他资源，以支持未来劳动力的转型。拟议工作的智力价值是（a）开发可扩展的资源，以增加数据科学和材料科学与工程边界研究中的 UG 经验和学习，（b）发展从事材料信息学的导师和 UG 研究人员社区(c) 通过劳动力发展和材料信息学培训，提高数据科学工具的利用率，以解决 MS&E 及相关领域的关键问题。拟议工作的更广泛影响是（a）自由传播材料信息学的有利课程和工具，（b）培训工作人员、导师和研究生广泛适用的研究和专业技能，以及（c）为材料信息学开发多元化的实践社区材料信息学研究人员。该项目将通过主要支持通常研究资源有限的 UG 机构和社区学院，培养一支能够掌握先进材料信息学的新劳动力队伍，特别是对于代表性不足的群体。该项目由计算机和信息科学与工程理事会高级网络基础设施办公室资助，数学和物理科学理事会材料研究部也提供资金。该奖项反映了 NSF 的法定使命，并被认为是值得的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来获得支持。