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泛函理论 (DFT) 计算联系起来。具有高居里转变温度的钙钛矿新化学成分的发现是本研究的模板；尽管正在开发的计算方法对于任何类别的材料问题都是通用的。该方法利用和开发统计学习和图论方法来发现已知材料中的行为模式，这些模式可用于生成设计规则来识别新化合物中的结构-性质关系；并将用于指导 DFT 计算并将其瞄准具有所需特性的特定化学物质。具体来说，该项目正在开发一种基于信息学的基于规则的设计方法，通过探索巨大的化学空间来识别高温压电钙钛矿的新化学成分，否则仅通过实验和/或计算方法。这项工作的智力影响将是将计算材料科学的传统用途从构建大型数据存储库（信息学主要用作化合物搜索引擎）转变为使用数据科学方法作为学习引擎来发现新的物理学，并提出了对有前景的化学物质进行详细计算的新方向，否则这些化学物质将无法被识别。这项工作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设施（CI），其中包括模型、材料元数据和模拟功能，可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、多学科培训，对高中生和教师的推广，以及将代表性不足的群体纳入教育和推广工作。非技术摘要 原子尺度材料建模领域利用了基于量子力学的复杂理论，称为密度泛函理论或简称 DFT，这些理论在计算高效的软件中实现，但仍然是计算密集型的。该项目利用了一种称为材料信息学的新兴方法，该方法能够通过探索数据的统计性质来检测和提取结构-性质相关性，而不必利用预先存在的理论公式。在该项目中，材料信息学用于识别具有高温电子设备最佳性能的新材料，而无需对每种可能的材料结构和成分进行计算。开发的工具将通过网络基础设施平台（我们称之为“材料数据铸造厂”）广泛提供。该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，学生们可以使用该项目中开发的工具来分析真实数据和问题。在佛罗里达大学，外展活动将通过佛罗里达大学学生科学培训计划重点针对高中生，而在爱荷华州立大学，我们将利用爱荷华州 EPSCoR 计划来访问全国社区和部落学院网络。我们的计划是利用 Materials Data Foundry 的教育功能与教师建立联系，而教师将在该项目的过程中接触到数百名学生。