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.

在化学系的支持以及数学科学系和高级网络基础设施办公室的部分共同资助下，特拉华大学的 Karl Booksh 和 Sharon Neal 教授正在组织一个研讨会，以确定与化学相关的数据分析的新领域科学发现的测量。 科学仪器的进步通过收集大量数据帮助实现了“数据革命”，既可以详细地探测单个样本，也可以从大量有趣的样本中收集数据片段。 数据分析策略通常打着机器学习和人工智能的旗号，提供新工具来帮助研究人员从此类数据收集中获得有价值的科学见解。 不幸的是，这些工具和具有应用领域知识的研究人员之间仍然存在适应性差距，他们将受益于更先进的数据分析方法的可用性。 本研讨会旨在确定化学研究的聚合领域，这些领域最能从新的尖端数据分析工具中受益，并确定减少具有相关应用领域知识的研究人员广泛和快速采用此类工具的障碍的方法。 该研讨会还将探讨培训学生有价值的数据分析技能的需求和方法。该研讨会将汇集化学科学及相关领域（例如环境分析、生物化学、材料科学和国家安全监测）的研究人员以及新颖数据分析（例如机器学习、人工智能、图论）的专家。 参与者将探讨融合应用领域中紧迫的科学问题与独特解决这些问题的新分析方法的潜力之间的关系。 例如，图论或网络分析如何提前预测新催化剂和更有效的反应序列？ 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？ 传感器阵列如何才能最好地适应以提高对动态环境过程化学的理解和预测？ 参与者将努力确定哪些领域需要合作来推进融合研究以及促进建立这些合作的方法。 研讨会还将考虑培养精通测量科学和数据分析的研究队伍的教育和培训需求。 该研讨会将采用虚拟和/或面对面的形式吸引来自学术、工业和政府部门的大约 30 名思想领袖。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进行评估，被认为值得支持审查标准。