Collaborative Research: NRT-DESE: Interdisciplinary Research Traineeships in Data-Enabled Science and Engineering of Atomic Structure

合作研究：NRT-DESE：数据支持的原子结构科学与工程跨学科研究实习

• 批准号: 1633587
• 负责人: Brian Reich
• 金额: $ 255.56万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633587&HistoricalAwards=false
• 关键词: Collaborative; Research; NRT; DESE; Interdisciplinary

In many important natural, synthetic and engineered materials, functionality and properties emerge at or below the nanoscale; however, quantifying atomic structure (i.e., local chemistry, bonding, atomic positions, spatial correlations and topology) in three-dimensions, through time and varying length scales, remains a challenge. This National Science Foundation Research Traineeship (NRT) award to North Carolina State University and North Carolina Central University will develop a new educational paradigm for Data-Enabled Science and Engineering of Atomic Structure (SEAS) to address the demand for a new generation of interdisciplinary, data-driven scientists who can apply advanced statistical methods to atomic-structure data generated from cutting-edge analytical and computational experiments. The research pioneered by these students will ultimately lead to a greater understanding of how the atomic structures of materials govern their physical properties (e.g. electronic, optical, mechanical). The project anticipates training at least forty (40) MS and PhD students over the five-year grant, including twenty (20) funded trainees, from materials science, physics, statistics and applied mathematics. With large investments in our national scientific infrastructure at both Federal laboratories and universities, a new and evolving generation of atomically sensitive instruments has opened new opportunities for next-generation science. Parallel to these developments in measurement sciences, great strides have been made in computational materials science, which are providing unprecedented opportunities for predictive materials design. The SEAS effort will develop a new graduate-training model, responding to the emergence and rapid growth of this critical interdisciplinary research at the interface of materials and data science and contributing directly and indirectly to the national Materials Genome Initiative (MGI), a multi-agency initiative spearheaded by the White House that advances the U.S. economy by enabling faster deployment of new materials. The SEAS traineeship program will immerse graduate students in a unique interdisciplinary curricular and research environment in which the trainees will be team-mentored by a diverse group of faculty and external industry and national laboratory scientists. The students will design professional development portfolios that will include laboratory rotations, interdisciplinary research group activities, internships, research training modules, communication training, and leadership-training activities. SEAS will promote and enhance diversity within the traineeship and larger professional community, and an integral part of the traineeship will be a bridge-to-the-PhD program across the partnering institutions aimed at better preparing underrepresented students to succeed in a research-intensive PhD program.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.

在许多重要的天然、合成和工程材料中，功能和性质出现在纳米级或纳米级以下;然而，量化原子结构（即，局部化学、键合、原子位置、空间相关性和拓扑学）在三维空间中通过时间和不同长度尺度的变化仍然是一个挑战。这项授予北卡罗来纳州州立大学和北卡罗来纳州中央大学的国家科学基金会研究培训计划（NRT）将为原子结构的数据科学与工程（SEAS）开发一种新的教育范式，以满足对新一代跨学科，数据驱动的科学家的需求，他们可以将先进的统计方法应用于从尖端分析和计算实验中产生的原子结构数据。这些学生开创的研究最终将导致对材料的原子结构如何管理其物理特性（例如电子，光学，机械）的更深入的了解。该项目预计在五年的赠款中培训至少四十（40）名硕士和博士生，其中包括二十（20）名受资助的学员，来自材料科学，物理学，统计学和应用数学。随着对我们在联邦实验室和大学的国家科学基础设施的大量投资，新一代和不断发展的原子敏感仪器为下一代科学开辟了新的机会。与测量科学的这些发展相平行，计算材料科学也取得了长足的进步，这为预测性材料设计提供了前所未有的机会。SEAS的努力将开发一种新的研究生培养模式，以应对材料和数据科学界面上这一关键跨学科研究的出现和快速增长，并直接和间接地为国家材料基因组计划（MGI）做出贡献，这是一项由白宫牵头的多机构计划，通过更快地部署新材料来推动美国经济。该海实习计划将沉浸在一个独特的跨学科课程和研究环境中的研究生，其中学员将由教师和外部行业和国家实验室科学家的多元化群体的团队指导。学生将设计专业发展组合，其中包括实验室轮换，跨学科研究小组活动，实习，研究培训模块，沟通培训和领导力培训活动。SEAS将促进和加强实习和更大的专业社区内的多样性，实习的一个组成部分将是跨合作机构的桥梁博士课程，旨在更好地准备代表性不足的学生在研究密集型博士课程中取得成功。NSF研究实习（NRT）计划旨在鼓励开发和实施大胆，为STEM研究生教育培训提供新的潜在变革模式。该培训轨道致力于在高优先级的跨学科研究领域的STEM研究生的有效培训，通过全面的培训模式，是创新的，以证据为基础，并与不断变化的劳动力和研究需求保持一致。

项目成果

Accounting for Location Measurement Error in Imaging Data With Application to Atomic Resolution Images of Crystalline Materials

• DOI: 10.1080/00401706.2021.1905070
• 发表时间: 2019-10
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: M. J. Miller;M. Cabral;E. Dickey;J. Lebeau;B. Reich

Active subspace analysis and uncertainty quantification for a polydomain ferroelectric phase-field model

多域铁电相场模型的主动子空间分析和不确定性量化

• DOI: 10.1177/1045389x19853636
• 发表时间: 2019
• 期刊: Journal of Intelligent Material Systems and Structures
• 影响因子: 2.7
• 作者: Leon, Lider S;Miles, Paul R;Smith, Ralph C;Oates, William S
• 通讯作者: Oates, William S

Soft Matter Informatics: Current Progress and Challenges

• DOI: 10.1002/adts.201800129
• 发表时间: 2018-11
• 期刊: Advanced Theory and Simulations
• 影响因子: 3.3
• 作者: James S. Peerless;Nina J. B. Milliken;Thomas J. Oweida;Matthew D. Manning;Yaroslava G. Yingling

A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns

• DOI: 10.1016/j.ultramic.2018.03.004
• 发表时间: 2018-05-01
• 期刊: ULTRAMICROSCOPY
• 影响因子: 2.2
• 作者: Xu, W.;LeBeau, J. M.
• 通讯作者: LeBeau, J. M.

Analysis of a multi-axial quantum informed ferroelectric continuum model: Part 1—uncertainty quantification

多轴量子信息铁电连续体模型分析：第 1 部分 — 不确定性量化

• DOI: 10.1177/1045389x18781023
• 发表时间: 2018
• 期刊: Journal of Intelligent Material Systems and Structures
• 影响因子: 2.7
• 作者: Miles, Paul;Leon, Lider;Smith, Ralph C;Oates, William S
• 通讯作者: Oates, William S