Collaborative Research - CDMR: Informatics Guided Data Driven Computational Design of Multifunctional Materials

协作研究 - CDMR：信息学引导的数据驱动的多功能材料计算设计

• 批准号: 1307840
• 负责人: Susan Sinnott
• 金额: $ 18.25万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1307840&HistoricalAwards=false
• 关键词: Collaborative; Research; CDMR; Informatics; Guided

Technical Abstract This project will establish a new approach for materials simulation and modeling that links informatics methods to Density Functional Theory (DFT) calculations. The discovery of new chemistries of perovskites that possess high Curie transition temperatures are the template for this study; although the computational methodology that is being developed is generic to any class of materials problems. The approach exploits and develops statistical learning and graph theoretic methodologies to discover patterns of behavior among known materials that can be used to generate design rules for identifying structure - property relationships in new compounds; and will serve to guide and target DFT calculations to specific chemistries with the desired properties. Specifically, the project is developing a rule-based design approach, based on informatics, to identify new chemistries of high temperature piezoelectric perovskites by exploring vast chemical space, so large, that it would otherwise have been prohibitive to study by solely experimental and/or computational methods. The intellectual impact of this work will be to transform the traditional use of computational materials science from one of building large data repositories where informatics is primarily used as a search engine for compounds, to one where data science methods are used as a learning engine to uncover new physics and suggest new directions for conducting detailed computations on promising chemistries that would otherwise remain unidentified. The broader impacts of this work is to build a data science cyberinfrastructure (CI) that called a "Materials Data Foundry" that will include models, materials metadata, and simulation capabilities that will be broadly accessible through a secure cyber infrastructure platform. The project will also involve the multi-institutional, multi-disciplinary training of graduate and undergraduate students, outreach to high-school students and teachers, and inclusion of underrepresented groups in both education and outreach efforts. Non-Technical Abstract The field of atomistic scale materials modeling makes use of sophisticated theories based on quantum mechanics, called Density Functional Theory or DFT for short, that are implemented in computationally efficient software that are, nonetheless, computationally intensive. This project makes use of a new and emerging methodology called Materials Informatics that is able to detect and extract structure-property correlations by exploring the statistical nature of data without necessarily utilizing pre-existing theoretical formulas. In this project materials informatics is used to identify new materials with optimum properties for high-temperature electronic devices without having to carry out calculations for every possible material structure and composition. The tools developed will be made broadly available through a cyberinfrastructure platform, we term a "Materials Data Foundry". This web portal will also facilitate joint online courses between Iowa State University (ISU) and the University of Florida (UF), where the students use the tools developed in the project to analyze real data and problems. At UF, outreach will be focused on high school students through the UF Student Science Training Program while at ISU we will leverage the Iowa EPSCoR program to have access to a national network of community and Tribal Colleges. Our plan is use the educational capabilities of the Materials Data Foundry to connect with teachers who in turn will reach hundreds of students over the course of this project.

技术摘要该项目将建立一种新的材料模拟和建模方法，将信息学方法与密度功能理论（DFT）计算联系起来。发现具有较高居里过渡温度的钙钛矿的新化学物质是这项研究的模板。尽管正在开发的计算方法对于任何类别的材料问题都是仿制的。该方法利用并开发了统计学习和图理论方法，以发现已知材料之间的行为模式，这些模式可用于生成设计规则以识别结构 - 新化合物中的属性关系；并将用于指导和靶向DFT计算到具有所需特性的特定化学物质。具体而言，该项目正在开发一种基于信息学的基于规则的设计方法，以通过探索庞大的化学空间（如此之大）来识别高温压电钙化化学的新化学方法，以至于完全通过实验性和/或计算方法进行研究，否则将是令人难以置信的。这项工作的智力影响将是将计算材料科学的传统使用从一个建立大型数据存储库中的一个转换，这些数据存储库主要用作化合物的搜索引擎，将数据科学方法用作学习引擎来揭示新的物理学并提出新的方向，并建议对有希望的化学详细计算进行详细计算，以否则将是不明显的。这项工作的更广泛的影响是建立一个称为“材料数据铸造厂”的数据科学网络基础架构（CI），其中包括模型，材料元数据和模拟功能，这些功能将通过安全的网络基础设施平台广泛访问。该项目还将涉及对研究生和本科生的多机构，多学科的培训，向高中生和老师的宣传，以及在教育和推广工作中包括代表性不足的团体。非技术摘要原子量表材料建模的领域可利用基于量子力学（称为密度功能理论或简短的DFT）的复杂理论，这些理论在计算高效的软件中实现，尽管如此，这些软件还是在计算上进行的。该项目利用一种称为材料信息学的新的和新兴的方法论，能够通过探索数据的统计性质来检测和提取结构 - 特质相关性，而无需使用预先存在的理论公式。在此项目中，材料信息学用于确定具有最佳特性的高温电子设备的新材料，而无需对所有可能的材料结构和组成进行计算。开发的工具将通过网络基础设施平台广泛提供，我们称为“材料数据铸造厂”。该网络门户网站还将促进爱荷华州立大学（ISU）和佛罗里达大学（UF）之间的联合在线课程，该课程使用该项目中开发的工具来分析实际数据和问题。在UF，外展活动将通过UF学生科学培训计划专注于高中生，而在ISU，我们将利用爱荷华州EPSCOR计划来访问国家社区和部落学院网络。我们的计划是利用材料数据铸造厂的教育能力与教师建立联系，而教师又将在这个项目的过程中吸引数百名学生。