Collaborative Research: SSMCDAT2020: Solid-State and Materials Chemistry Data Science Hackathon

该奖项在材料研究部、数学科学部和多学科活动办公室的支持下，为固态材料化学和数据科学领域的研究人员赞助组织了一场“数据科学黑客马拉松”。本次活动，SSMCDAT2020，汇集了来自这些领域的成员，通过数据密集型研究进一步科学，以便在解决与固态材料化学相关的挑战性问题方面取得进展。团队在为期三天的活动中一起工作，既可以作为团队工作，也可以作为一个大群体来完成一系列研究项目。黑客马拉松为材料和数据科学家之间的长期合作奠定了基础。材料基因组计划（MGI）于2011年推出，其目标是以两倍的速度和一小部分的成本开发和部署新材料。该倡议的关键原则是：(a)材料“基因”存在，对材料“基因组”的熟练操作将导致先进材料的快速发现和部署，以及(b)实验性材料研究过于昂贵和缓慢，无法成为探索和发现的主要工具。基于这些原因，MGI建议通过明智地使用计算材料科学来减少调查。这种计算技术的发展已经改变了这个领域，但解锁材料基因组仍然面临着重大挑战。为了应对这些挑战，这场以固态材料化学为重点的“黑客马拉松”旨在扩大MGI制定的方法，以涵盖快速发展的数据科学领域。事实上，这种方法的早期采用者已经积累了许多令人印象深刻的概念证明。随着固态材料化学和数据科学研究人员之间强有力的合作伙伴关系，可以实现MGI目标的重大进展。这个“数据科学黑客马拉松”基本上是一个跨学科的努力。固态材料化学研究人员接受数据科学工具应用方面的培训，了解各种方法的优点和局限性。数据科学家暴露于材料可用数据的广度，以及最紧迫的固态材料研究挑战。特别注意有前途的研究生和博士后的参与，以及代表性不足的群体。该奖项反映了美国国家科学基金会的法定使命，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。

This award, with support from the Division of Materials Research, the Division of Mathematical Sciences and the Office of Multidisciplinary Activities, sponsors the organization of a "data science hackathon" for researchers in the fields of solid-state materials chemistry and data science. This event, SSMCDAT2020, bring together members from these fields to further science through data-intensive research in order to make advances toward solving challenging problems relevant to solid-state materials chemistry. Teams work together for a three-day event, working both as teams and as a large cohort on a set of research projects. The hackathon lays a foundation for long-lasting collaborations between materials and data scientists.The Materials Genome Initiative (MGI) was introduced in 2011 with the goal of developing and deploying new materials at twice the speed and a fraction of the cost. Key tenets of the initiative are the ideas that (a) materials "genes" exist and skilled manipulation of the materials "genome" will lead to rapid discovery and deployment of advanced materials, and (b) experimental materials research has been far too costly and slow to be the primary vehicle for exploration and discovery. For these reasons, the MGI proposes that investigations should be minimized by judicious use of computational materials science. The development of such computational techniques has transformed the field, but unlocking the materials genome still faces major challenges. To address these challenges, this solid-state materials chemistry-focused "hackathon" is designed to broaden the approach laid out in the MGI to encompass the rapidly evolving field of data science. Indeed, early adopters of this approach have amassed numerous impressive proofs of concept. With strong partnerships between solid-state materials chemistry and data science researchers, significant advances toward accomplishing the goals of the MGI can be achieved.This "data science hackathon" is a fundamentally interdisciplinary endeavor. Solid-state materials chemistry researchers are trained in the application of data science tools, becoming informed of the advantages and limitations of various approaches. Data scientists are exposed to the breadth of materials data available, as well as the most pressing solid-state materials research challenges. Special attention is paid to have participation from promising graduate students and postdocs, as well as under-represented groups.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.