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arXiv 2406.08142physics.ed-phcond-mat.mtrl-sci
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一种用于课程式本科生研究体验(CURE)的计算材料科学范式

A computational materials science paradigm for a Course-based Undergraduate Research Experience (CURE)

David A. Strubbe

AI总结:

本文提出一种基于 nanoHUB.org 网络模拟工具的计算材料科学 CURE 范式,使本科生在课堂中按协议计算材料数据、构建班级数据集并分析,已在多个课堂项目中成功应用。

摘要:

课程式本科生研究体验(CUREs)将研究的兴奋感带入课堂,以改善学习效果和学生在领域中的归属感。与典型的本科生研究相比,它们能够在学生学业更早阶段覆盖更多学生。其关键方面包括:学生学习并使用研究方法、为项目提供意见、生成新的研究数据,并分析这些数据以得出事先未知的结论。CUREs 在其他领域已很常见,但在材料科学与工程领域一直较为罕见。我提出一种计算材料科学 CUREs 的范式,该范式借助 nanoHUB.org 上基于网络的模拟工具实现,这些工具仅需极少的计算技能。在完成预备练习后,每位学生按照既定协议计算一组密切相关的材料中的一部分,为一个新颖的班级数据集做出贡献并对其进行分析,同时再计算一个自选的附加性质。该方法已在多个课堂项目中成功应用。

英文摘要:

Course-based Undergraduate Research Experiences (CUREs) bring the excitement of research into the classroom to improve learning and the sense of belonging in the field. They can reach more students, earlier in their studies, than typical undergraduate research. Key aspects are: students learn and use research methods, give input into the project, generate new research data, and analyze it to draw conclusions that are not known beforehand. CUREs are common in other fields but have been rare in materials science and engineering. I propose a paradigm for computational material science CUREs, enabled by web-based simulation tools from nanoHUB.org that require minimal computational skills. After preparatory exercises, students each calculate part of a set of closely related materials, following a defined protocol to contribute to a novel class dataset which they analyze, and also calculate an additional property of their choice. This approach has been used successfully in several class projects.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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