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

合作研究：SSMCDAT2020：固态和材料化学数据科学黑客马拉松

• 批准号: 1938729
• 负责人: Frank Curtis
• 金额: $ 3.74万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938729&HistoricalAwards=false
• 关键词: Collaborative; Research; SSMCDAT2020; Solid; State

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.

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，为固体材料化学和数据科学领域的研究人员组织了一场“数据科学黑客马拉松”。这次活动SSMCDAT2020将这些领域的成员聚集在一起，通过数据密集型研究进一步科学，以便在解决与固体材料化学相关的具有挑战性的问题方面取得进展。团队在为期三天的活动中合作，既作为团队合作，也作为一系列研究项目的一个大群体。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，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。该倡议的主要原则是这样的想法：(A)材料“基因”的存在和对材料“基因组”的熟练操纵将导致快速发现和部署先进材料，以及(B)实验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。出于这些原因，MGI建议通过合理使用计算材料科学来最大限度地减少调查。这种计算技术的发展改变了这一领域，但解锁材料基因组仍然面临重大挑战。为了应对这些挑战，这场专注于固体材料化学的“黑客马拉松”旨在扩大MGI中提出的方法，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大量令人印象深刻的概念证据。通过固态材料化学和数据科学研究人员之间的紧密合作，可以在实现MGI目标方面取得重大进展。这场“数据科学黑客松”从根本上说是一项跨学科的努力。固态材料化学研究人员接受了数据科学工具应用方面的培训，了解各种方法的优势和局限性。数据科学家面临着大量可用的材料数据，以及最紧迫的固态材料研究挑战。特别关注有前途的研究生和博士后，以及代表不足的群体的参与。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。