CyberTraining: CIC: The Texas A&M University Computational Materials Science Summer School (CMS3)
CyberTraining: CIC: The Texas A&M University Computational Materials Science Summer School (CMS3)
批准号:
1829799
负责人:
Ahmed-Amine Benzerga
金额:
$49.98万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-01 至 2023-08-31
中文摘要
硬件和软件基础设施的进步正在迅速使对材料和材料现象进行逼真的模拟成为可能,从而更好地理解它们的行为。模拟是实现材料基因组计划(MGI)的关键要素之一,该计划要求减少开发材料所需的时间和资源,以实现潜在的变革性技术。为了推动计算材料科学(CMS)蓬勃发展,有必要为下一代研究人员提供尽可能多的计算技术的广泛接触。该项目汇集了材料科学和工程以及先进网络基础设施(CI)的研究人员,建立了计算材料科学暑期学校(CMS3),旨在培训研究生和初级科学家和工程师使用一些最先进和广泛使用的计算材料科学模拟工具。CMS3提供的培训将为参与的学生提供必要的知识和技能,以推动模拟材料研究的前沿。此外,CMS3将通过培训科学家和工程师来发现和开发将使国家重要技术成为可能的材料,从而有助于MGI维持国家整体经济竞争力和安全的使命。该奖项利用现有的网络基础设施,通过本地和远程培训扩大参与,包括来自代表性不足群体的参与。专家讲师从学术,国内和国际机构和国家实验室中选择,涵盖三个模块:连续微观力学,介观模拟和原子建模,以及数据科学的总体主题。该学院的目标是20名现场和多达80名远程研究生和博士后研究人员,包括行业参与者。CMS3的活动是(i)在国家实验室和学术机构之间建立一个CMS网络,以利用CMS社区的专业知识,在多个尺度上发展下一代材料研究队伍;(ii)制定与暑期学校形式相适应的CMS课程,其中包括利用和支持先进的CI,以有效扩大一系列实践;(iii)在专业领域组织较短的种子项目,如位错动力学、相场建模和数据支持材料科学;(iv)将理论基础和实践训练整合到德克萨斯农工大学和其他地方的研究生课程和继续教育中;(v)通过虚拟和增强现实工具引入沉浸式可视化,帮助不同背景和学习风格的学生解读复杂的材料数据。与此项目相关的课程材料和软件代码尚未由原始开发人员传播,它们保存在GitHub存储库中。这确保了所有发布的材料和包装都被保存下来,以保持历史背景和广泛的访问。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。
英文摘要
Advances in both hardware and software infrastructure are quickly making it possible to carry out realistic simulations of materials and materials phenomena that can lead to a better understanding of their behavior. Simulations constitute one of the key ingredients necessary to realize the Materials Genome Initiative (MGI), which calls for the reduction in time and resources necessary to develop the materials needed to enable potentially transformative technologies. To push the thriving field of Computational Materials Science (CMS) forward it is necessary to provide the next generation research workforce with a broad exposure to as many computational techniques as possible. This project brings together researchers in materials science and engineering, and in advanced cyber-infrastructure (CI) to establish the Computational Materials Science Summer School (CMS3) that aims to train graduate students and junior scientists and engineers in some of the most advanced and widely used computational materials science simulation tools. The training provided by CMS3 will equip participating students with the knowledge and skills necessary to push the frontiers of simulation-enabled materials research. Furthermore, CMS3 will contribute to MGI's mission of maintaining the Nation's overall economic competitiveness and security by training the scientists and engineers that will discover and develop the materials that will make technologies of national importance possible.This award leverages existing cyber-infrastructure to expand participation, including from underrepresented groups, through local and remote training. Expert instructors are selected from academic, domestic and international institutions, and national laboratories to cover three modules: continuum micromechanics, mesoscopic simulation and atomistic modeling, along with an overarching theme of data science. The school targets 20 on-site and up to 80 remote graduate and post-doctoral researchers, including industry participants. The activities of CMS3 are to (i) establish a CMS network among national laboratories and academic institutions to leverage the expertise of the CMS community at large in developing the next-generation workforce in materials research across multiple scales; (ii) develop a CMS curriculum, commensurate with a summer school format, that involves utilizing and supporting advanced CI for effective scale-up of a series of practica; (iii) organize shorter seed programs in specialized areas, such as dislocation dynamics, phase-field modeling and data-enabled materials science; (iv) integrate the theoretical foundations and practical training into the graduate curriculum and continuing education at Texas A&M University and elsewhere; and (v) introduce immersive visualization through virtual and augmented reality tools to help students with different backgrounds and learning styles interpret complex material data. Course material and software codes associated with this project not already disseminated by the original developers are maintained in a GitHub repository. This ensures that all released materials and packages are preserved to maintain historical context and broad access.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.
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