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，以培训一支在材料信息学中研究和应用机器学习(ML)的熟练劳动力。材料信息学的ML正在以前所未有的能力迅速改变材料科学与工程(MS&Amp；E)，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，并加快材料设计。正如美国国家科学院和矿物、金属和材料学会(TMS)最近的多项研究指出的那样，有必要培训材料信息学的下一代劳动力，以实现其通过先进材料改善人类条件的巨大潜力。不幸的是，材料信息学的ML几乎完全没有出现在今天的本科生材料课程中。然而，信息学工具的力量，加上它们在MS&amp；E中的快速发展和相对新颖性，创造了一个机会，让本科生(UGS)通过有影响力的研究来主动学习。在这一动机下，该项目将创建一个新的基础设施和一个生态系统，供美国各地的UGS在材料信息学中使用应用ML进行研究和培训，称为信息学恶作剧。臭鼬团队由从事材料信息学研究的Mentor/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工作的实践社区。ML和材料信息学的低成本和可获得性为Skunkworks创造了一个机会，让他们能够吸引研究资源有限的导师和学生，特别是在为代表不足的群体提供服务的机构。Skunkworks是一种可持续和可扩展的方法，可以通过培训一支精通材料信息学ML研究和应用的多样化劳动力来满足这一未得到满足的需求。该项目将提供免费提供的(A)培训UGS相关ML、材料信息学和研究专业发展的课程，(B)扩充现有ML包使其可供UG使用的软件工具，以及(C)真实和适当水平的研究问题。拟议的工作还将发展一个实践社区，使一个富有成效的Mentor/UG研究团队网络能够有效和协作地利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，以在数据科学、材料科学和工程的边界增加普遍关注的研究经验和学习；(B)培养从事材料信息学研究的导师和普遍关注的研究人员群体；(C)通过劳动力发展和材料信息学培训，更多地利用数据科学工具解决MS&amp；E和相关领域的关键问题。拟议工作的更广泛影响是：(A)自由传播有利于材料信息学的课程和工具，(B)在广泛适用的研究和专业技能方面培训工作人员、导师和一般事务人员，以及(C)为材料信息学研究人员发展多样化的实践社区。该项目将通过主要支持通常研究资源有限的UG机构和社区大学，促进发展一支能够从事先进材料信息学的新劳动力队伍，特别是针对代表性不足的群体。这个项目由计算机和信息科学与工程局的高级网络基础设施办公室资助，数学和物理科学局的材料研究部也提供资金。这个奖项反映了NSF的法定使命，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。

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.