Scientific Discovery from Chemical Data Analyses

化学数据分析的科学发现

• 批准号: 2011061
• 负责人: Karl Booksh
• 金额: $ 5万
• 依托单位: University of Delaware
• 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: 2020-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011061&HistoricalAwards=false
• 关键词: Scientific; Discovery; Chemical; Data; Analyses

With support from the Division of Chemistry and partial co-funding from the Divisions of Mathematical Sciences and the Office of Advanced Cyberinfrastructure, Professors Karl Booksh and Sharon Neal at the University of Delaware are organizing a workshop to identify new frontiers in data analyses associated with chemical measurements for scientific discovery. Advances in scientific instrumentation have helped enable the "data revolution" through collection of vast amounts of data, both to probe a single sample in exquisite detail and to collect snippets of data from a wide array of interesting samples. Data analytic strategies, often under the banner of machine learning and artificial intelligence for example, offer new tools to help researchers gain valuable scientific insights from such data collections. Unfortunately, there remains an adaptation gap between these tools and researchers with application domain knowledge who would benefit from the availability of more advanced data analysis methods. This workshop seeks to identify convergent areas of chemical research that would most benefit from new cutting-edge tools for data analysis and to identify means of reducing the impediments to broad and rapid adoption of such tools by researchers with relevant application domain knowledge. The workshop will also explore needs and means for training of students in valuable data analysis skills. The workshop will bring together researchers in the chemical sciences and related areas (e.g., environmental analysis, biological chemistry, materials science, and national security monitoring) and experts in novel data analytics (e.g., machine learning, artificial intelligence, graph theory). Participants will explore the relationship between pressing scientific questions in convergent application fields and the potential for new analytical methods that uniquely address these questions. For example, how might graph theory or network analyses advance prediction of new catalysts and more efficient reaction sequences? What insights might machine learning or artificial intelligence derive from high-resolution, multivariate images of catalytic surfaces? How can sensor arrays be best adapted to improve understanding and prediction of the chemistry of dynamic environmental processes? Participants will strive to identify where collaborations are necessary to advance convergence research and means of facilitating establishment of those collaborations. The workshop will also consider the education and training needs for creating a research workforce versed in both measurement science and data analysis. The workshop will engage approximately 30 thought-leaders spanning academic, industrial, and governmental sectors using virtual and/or in-person formats.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.

在数学科学和高级网络基础设施办公室的化学和部分共同资助的支持下，特拉华大学的Karl Booksh和Sharon Neal教授正在组织一个研讨会，以确定与科学发现化学测量相关的数据分析中的新界面。 科学仪器的进步有助于通过收集大量数据来实现“数据革命”，既可以细节探究单个样本，又可以从各种有趣的样本中收集数据片段。 例如，在机器学习和人工智能的旗帜下，数据分析策略提供了新的工具来帮助研究人员从此类数据收集中获得宝贵的科学见解。 不幸的是，这些工具与具有应用领域知识的研究人员之间的适应性差距仍然存在差距，这些知识将受益于更先进的数据分析方法。 该研讨会旨在确定化学研究的收敛领域，这些领域将最大程度地从新的尖端工具中受益于数据分析，并确定降低具有相关应用领域知识的研究人员的广泛和快速采用此类工具的障碍。 该研讨会还将探索在有价值的数据分析技能上培训学生的需求和手段。该研讨会将汇集化学科学及相关领域的研究人员（例如，环境分析，生物化学，材料科学和国家安全监测）以及新型数据分析方面的专家（例如，机器学习，人工智能，图理论）。 参与者将探讨收敛应用领域中的科学问题与独特解决这些问题的新分析方法之间的关系。 例如，图理论或网络分析如何提高新催化剂和更有效的反应序列的预测？ 机器学习或人工智能可以从催化表面的高分辨率多元图像中获得哪些见解？ 如何最好地适应传感器阵列以提高对动态环境过程化学的理解和预测？ 参与者将努力确定需要在何处进行合作，以推进融合研究和促进这些合作的促进手段。 该研讨会还将考虑创建精通测量科学和数据分析的研究人员的教育和培训需求。 该研讨会将使用虚拟和/或面对面的格式与大约30个跨越学术，工业和政府部门的思想领导者互动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子评估来支持的，并具有更广泛的影响。