CDI Type I: Collaborative Research: Integration of relational learning with ab-initio methods for prediction of material properties

CDI I 型：协作研究：将关系学习与从头开始的方法相结合，用于预测材料特性

• 批准号: 0941043
• 负责人: Gerbrand Ceder
• 金额: $ 30.54万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941043&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; Research; Integration

The objective of this collaborative research is to apply computational thinking to materials science with the goals of revealing hidden rules about materials structure and properties and providing efficient computational and statistical tools for modeling large systems with interacting elements. The approach combines materials science with statistical learning. The research is driven by two key problems in materials development, crystal structure prediction and the inverse problem in materials science whereby one postulates the desired properties and finds the composition and arrangement of atoms that result in those properties. The investigators seek to design principled Bayesian models for relational data, coupled with efficient inference methods.With respect to intellectual merit, the integrative approach is a significant departure from current methods for materials research. Ab initio computation has begun to show promise for materials development, but its integration with statistical learning holds the promise of leading to novel approaches that can utilize massive amounts of materials data. Further, extracting knowledge from massive relational data presents opportunities for machine learning research. The research addresses common challenges in many disciplines and provides new mathematical frameworks and computational tools.With respect to broader impacts, the research has the potential to enhance materials research and, ultimately, lead to the development of better materials. The application focus on materials for energy is timely and important. The investigators plan to recruit women and other students from underrepresented groups into their research teams. Results will be disseminated through education and a cyber-based platform, exposing computer science students to engineering applications.

这项合作研究的目标是将计算思维应用于材料科学，旨在揭示材料结构和性能的隐藏规则，并为具有相互作用元素的大型系统建模提供有效的计算和统计工具。 该方法将材料科学与统计学习相结合。 该研究由材料开发中的两个关键问题驱动，晶体结构预测和材料科学中的逆问题，即假设所需的属性并找到导致这些属性的原子的组成和排列。 研究人员寻求设计原则贝叶斯模型的关系数据，再加上有效的推理方法。关于智力的优点，综合的方法是一个显着偏离目前的材料研究方法。 从头计算已经开始显示出材料开发的前景，但它与统计学习的集成有望导致新的方法，可以利用大量的材料数据。 此外，从大量关系数据中提取知识为机器学习研究提供了机会。 该研究解决了许多学科的共同挑战，并提供了新的数学框架和计算工具。就更广泛的影响而言，该研究有可能加强材料研究，并最终导致更好的材料的开发。 能源材料的应用重点是及时和重要的。 研究人员计划从代表性不足的群体中招募女性和其他学生加入他们的研究团队。 将通过教育和网络平台传播成果，使计算机科学学生接触工程应用。