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

该项目将开发一种可持续和可扩展的方法，以培训熟练掌握材料信息学中机器学习（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），它具有前所未有的能力来扩展材料数据库，改进材料模拟，挖掘文本，自动化材料研究和开发，并加速材料设计。正如最近的多项研究所指出的那样，包括来自美国国家科学院和矿物，金属材料学会（TMS）的研究，必须培训下一代材料信息学ML劳动力，以实现其通过先进材料改善人类状况的巨大潜力。不幸的是，材料信息学中的ML几乎完全没有出现在今天的本科材料课程中。然而，信息学工具的力量结合其快速发展和相对新奇的MS E创造了一个机会，让本科生（UG）通过有影响力的研究进行主动学习。有了这个动机，该项目将创建新的基础设施和生态系统，用于在美国各地使用材料信息学中的应用ML进行研究和培训，称为信息学Skunkworks。Skunkworks由导师/UG团队组成，在材料信息学方面进行研究。该项目为团队提供了新的资源，包括课程，软件和研究问题，以及一个实践社区，以支持研究和有效合作。ML和材料信息学的低成本和可访问性为Skunkworks创造了一个机会，使导师和学生能够利用有限的研究资源，特别是在为代表性不足的群体服务的机构。Skunkworks是一种可持续和可扩展的方法，可以通过培训掌握材料信息学ML研究和应用的多样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，以培训相关ML，材料信息学和研究专业发展的UG，（B）软件工具，增强现有ML包，使UG可访问，以及（c）真实和适当的研究问题。拟议的工作还将建立一个实践社区，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，以支持改造未来的劳动力。拟议工作的智力价值是（a）开发可扩展的资源，以增加UG在数据科学和材料科学与工程领域的研究经验和学习，（B）发展一个从事材料信息学研究的导师和UG研究人员社区，以及（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和相关领域的关键问题。拟议工作的更广泛的影响是：（a）免费传播材料信息学的课程和工具，（B）培训工作人员，导师和UG广泛适用的研究和专业技能，以及（c）为材料信息学研究人员开发一个多样化的实践社区。该项目将通过支持通常研究资源有限的大学机构和社区学院，培养一支能够掌握先进材料信息学的新员工队伍，特别是为代表性不足的群体。该项目由计算机和信息科学与工程局高级网络基础设施办公室资助，数学和物理科学局材料研究部也提供资金。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。

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.