REU Site: Computational Methods with applications in Materials Science

非技术摘要：计算方法和数据分析现在被广泛用于预测材料的性能，并理解材料实验和模拟的数据。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了解它们或参与这个迅速扩大的研究领域。由材料研究部和已建立的激励竞争研究计划(EPSCoR)共同资助的本科生研究体验(REU)网站解决了这一缺点，并为本科生参与者提供了一个独特的机会，让他们与密西西比州立大学(MSU)的跨学科计算研究中心计算科学中心(CCS)的一群研究人员一起参与有意义的研究。参与者将在密歇根州立大学花十周时间进行研究，由来自物理、化学、数学和统计以及工程学的教员指导。除了实践研究经验，参与者还将参加每周的研讨会和研讨会，涵盖与计算、材料和职业发展相关的主题。这个REU项目的很大一部分参与者来自历史上的黑人学院和大学，主要是来自全美的本科生机构。这个REU网站的毕业生将为在科学、工程或数学方面的研究生学习做好准备。技术摘要：这个REU的学生参与者在夏季与CCS的数学、统计、物理、化学和工程系的教师导师一起进行10周的多学科研究。学生首先接受研究项目所需的基础科学和计算技术方面的培训。额外的培训活动提高了参与者的计算技能和书面和口头演示技能。研究项目一般分为两个领域：材料模拟和设计，以及应用于材料的计算数据分析。具体项目包括(1)超荧光模型；(2)电子-电子关联；(3)原子间作用力的机器学习预测；(4)多功能材料设计；(5)依赖时间问题的数值方法；(6)自组装凝胶的模拟和设计；(7)多环芳烃的光学性质；(8)连续数据同化；(9)图像去噪和分割；以及(10)高维数据分析。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。学生每周参加关于科学和计算主题的小组会议、研讨会和培训课程。在暑期结束时，他们将项目的最终结果提交给REU小组和密歇根州立大学夏季本科生研究研讨会。这一奖项反映了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审查标准进行评估，被认为值得支持。

NON-TECHNICAL SUMMARY: 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. Jointly funded by the Division of Materials Research and the Established Program to Stimulate Competitive Research (EPSCoR), this Research Experiences for Undergraduates (REU) site addresses this shortcoming and provides a unique opportunity for undergraduate participants 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). Participants will spend ten weeks at MSU performing research mentored by faculty from Physics, Chemistry, Mathematics and Statistics, and Engineering. In addition to hands-on research experience, participants will attend weekly seminars and workshops covering topics related to computation, materials, and career development. A significant portion of participants for this REU project are recruited from historically black colleges and universities and primarily undergraduate institutions from across the United States. Graduates of this REU site will be well prepared for graduate study in science, engineering, or mathematics.TECHNICAL SUMMARY: The student participants recruited for this REU spend 10 weeks over the summer in multidisciplinary research with faculty mentors in Mathematics, Statistics, Physics, Chemistry, and Engineering from the 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 and written and oral presentation skills. The research projects fall into two general areas: materials simulation and design, and computational data analysis applied to materials. Specific projects include (1) modeling of superfluorescence; (2) electron-electron correlation; (3) machine learning prediction of interatomic forces; (4) multifunctional materials design; (5) numerical methods for time-dependent problems; (6) simulation and design of self-assembled gels; (7) optical properties of polycyclic aromatic hydrocarbons; (8) continuous data assimilation; (9) image denoising and segmentation; and (10) high-dimensional data analysis. REU participants are given access to supercomputing resources at the MSU High Performance Computing Collaboratory. The students participate in the weekly group meetings, seminars and training sessions on scientific and computational topics. 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.