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REU Site: Computational Methods with applications in Materials Science

REU 网站：计算方法及其在材料科学中的应用

基本信息

批准号：
1950208
负责人：
Hyeona Lim
金额：
$ 31万
依托单位：
Mississippi State University
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：
2020-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950208&HistoricalAwards=false
关键词：
REU Site Computational Methods applications

项目摘要

This project is jointly funded by the Division of Materials Research and the Established Program to Stimulate Competitive Research (EPSCoR).NON-TECHNICAL DESCRIPTION: Computational methods and data analysis are now widely used to predict the properties of materials and to understand data from materials experiments and simulations. While these computational techniques are commonly taught in graduate programs in science, math, and engineering, few undergraduate students have an opportunity to learn about them or participate in this rapidly expanding research field. This Research Experiences for Undergraduates (REU) Site addresses this shortcoming and provides a unique opportunity for undergraduate students to participate in meaningful research with a group of researchers from the Center for Computational Sciences (CCS), an interdisciplinary computational research center at Mississippi State University (MSU). A significant portion of participants for this REU program are recruited from historically black colleges and universities and primarily undergraduate institutions from across the United States. An annual one-day workshop for high school teachers offers the teachers guidance on research that they can perform with their own students. Graduates of this REU site are well prepared for graduate study in science, engineering, or mathematics.TECHNICAL DETAILS: The student participants recruited for this REU spend 10 weeks over the summer in multidisciplinary research with eight faculty mentors in Mathematics, Statistics, Physics, and Chemistry from CCS. The students are first trained in the basic science and computational techniques necessary for their research projects. Additional training activities improve the participants’ computational skills as well as written and oral presentation skills. The eight research projects are: (1) electron-electron correlation physics; (2) numerical Methods for Phase Field Models; (3) interfacial properties in high-performance composite materials; (4) materials research for rare earth-free permanent magnets; (5) image denoising and segmentation methods for material analysis; (6) Bayesian parameter estimation and model comparison; (7) photophysical properties of novel polycyclic aromatic hydrocarbons and their complexes; and (8) high-dimensional material data analysis. REU participants are given access to supercomputing resources at the MSU High Performance Computing Collaboratory. The students participate in weekly group meetings, seminars and training sessions on scientific and computational topics, and a Graduate Record Exam workshop. At the end of the summer they present the final results of their project to the REU group and at the MSU Summer Undergraduate Research Symposium.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.

该项目由材料研究部和促进竞争研究的既定计划（EPSCoR）共同资助。非技术描述：计算方法和数据分析现在被广泛用于预测材料的性能，并从材料实验和模拟中理解数据。虽然这些计算技术通常在科学、数学和工程的研究生课程中教授，但很少有本科生有机会学习它们或参与这个迅速发展的研究领域。这个本科生研究经验（REU）网站解决了这个缺点，并为本科生提供了一个独特的机会，让他们与一组来自密西西比州立大学（MSU）跨学科计算研究中心计算科学中心（CCS）的研究人员一起参与有意义的研究。这个REU项目的很大一部分参与者来自美国各地的传统黑人学院和大学，主要是本科院校。每年为高中教师举办的为期一天的研讨会为教师提供研究指导，他们可以与自己的学生一起进行研究。该REU的毕业生为科学，工程或数学的研究生学习做好了充分的准备。技术细节：为这个REU招募的学生参与者在夏季花10周时间在多学科研究中与来自CCS的数学，统计学，物理和化学的8位教师导师。学生们首先接受研究项目所需的基础科学和计算技术的培训。额外的培训活动提高了参与者的计算能力以及书面和口头表达能力。八个研究项目是：(1)电子-电子相关物理；(2)相场模型的数值方法；(3)高性能复合材料的界面性能；(4)无稀土永磁体材料研究；(5)材料分析图像去噪与分割方法；(6)贝叶斯参数估计与模型比较；(7)新型多环芳烃及其配合物的光物理性质；(8)高维材料数据分析。REU参与者可以访问密歇根州立大学高性能计算合作实验室的超级计算资源。学生参加每周的小组会议、科学和计算主题的研讨会和培训课程，以及研究生入学考试研讨会。在夏季结束时，他们向REU小组和密歇根州立大学夏季本科生研究研讨会提交了他们项目的最终结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。