SI2-SSI: Collaborative Research: A Robust High-Throughput Ab Initio Computation and Analysis Software Framework for Interface Materials Science
SI2-SSI:协作研究:用于界面材料科学的强大高通量从头计算和分析软件框架
基本信息
- 批准号:1550423
- 负责人:
- 金额:$ 30万
- 依托单位:
- 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:2016-09-01 至 2020-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
A set of techniques scientists call "ab initio" methods, which are derived from the fundamental laws of physics with minimal assumptions and approximations, has become a critical tool in the study and design of materials. With computing advances and software innovations, the automation of high-throughput ab initio calculations has, in particular, heralded an explosion of computed data for a large variety of materials. However, these high-throughput efforts are limited to specific properties. In contrast, materials interfaces, one of the fastest growing research areas in materials science and engineering, are showing an increasing relevance in many areas of materials applications such as catalysis and electronics. This project will develop a software framework that enables novel high-throughput interface materials investigations and design. The developed software platform will expand the genome of materials by including the computed interfacial properties of interface materials. This community-based software can potentially become a critical component of the Materials Genome Initiative and serve not just the large and diverse materials research community, but also the physics and chemistry communities. Besides featuring heavily in existing and planned courses taught by the Principal Investigators in their home institutions, the proposed framework will facilitate the training of undergraduates and graduates in the ab initio methodologies in other institutions as well. This project will also conduct public outreach activities to increase awareness of the importance of sustainable software development for data-driven interface materials science. The project will develop necessary workflow management, error correction schemes, and systematic analysis tools to support ab initio studies of thermodynamics, kinetics, diffusion, and electronic property of interface materials including hetero-structures and grain boundary. It targets developmental efforts on three key focus areas of great interest to interface materials science: (i) Ab initio thermodynamics of surfaces and interfaces; ii) Advanced methods for materials kinetics and diffusion at materials interfaces; and iii) Automated algorithms for structural construction of grain boundary and post data-processing and analysis. In doing so, this project will greatly expand the suite of interfacial materials properties that are amenable to a high-throughput ab initio treatment, paving the way for materials investigations and design in a broad spectrum of technological applications, including energy generation and storage, catalysis and electronics. In addition, by interfacing with classical-mechanics simulation codes, this framework will bridge the gap between the ab initio and classical force-field approach, which is expected to significantly advance the high-throughput simulations of materials interfaces. This award by the Advanced Cyberinfrastructure Division is jointly supported by the NSF Directorate for Mathematical and Physical Sciences (Division of Materials Research and Office of Multidisciplinary Activities).
科学家们称之为“从头算”方法的一套技术,它是从物理学的基本定律中推导出来的,具有最小的假设和近似,已经成为材料研究和设计的关键工具。随着计算技术的进步和软件的创新,高通量从头计算的自动化特别预示着各种材料的计算数据的爆炸。然而,这些高通量的努力仅限于特定的性质。相比之下,材料界面是材料科学和工程中发展最快的研究领域之一,在催化和电子等许多材料应用领域中表现出越来越大的相关性。该项目将开发一个软件框架,使新型高通量界面材料的研究和设计。开发的软件平台将通过包括界面材料的计算界面特性来扩展材料的基因组。这种基于社区的软件有可能成为材料基因组计划的关键组成部分,不仅服务于大型和多样化的材料研究社区,还服务于物理和化学社区。除了主要研究人员在其所在机构教授的现有和计划中的课程外,拟议框架还将促进其他机构对本科生和毕业生进行从头开始方法的培训。该项目还将开展公共宣传活动,以提高对可持续软件开发对数据驱动界面材料科学的重要性的认识。该项目将开发必要的工作流程管理,错误校正方案和系统的分析工具,以支持界面材料(包括异质结构和晶界)的热力学,动力学,扩散和电子性质的从头计算研究。它的目标是对界面材料科学非常感兴趣的三个关键重点领域的发展努力:(i)表面和界面的从头算热力学; ii)材料动力学和材料界面扩散的先进方法; iii)晶界结构构建的自动算法和后期数据处理和分析。在这样做的过程中,该项目将极大地扩展适合高通量从头计算处理的界面材料特性套件,为广泛的技术应用中的材料研究和设计铺平道路,包括能量产生和存储,催化和电子学。此外,通过与经典力学模拟代码的接口,该框架将弥合从头算和经典力场方法之间的差距,预计将显着推进材料界面的高通量模拟。该奖项由高级网络基础设施部门联合支持,由NSF数学和物理科学理事会(材料研究部门和多学科活动办公室)。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Yifei Mo其他文献
A cost-effective all-in-one halide material for all-solid-state batteries
一种用于全固态电池的具有成本效益的一体化卤化物材料
- DOI:
10.1038/s41586-025-09153-1 - 发表时间:
2025-06-25 - 期刊:
- 影响因子:48.500
- 作者:
Jiamin Fu;Changhong Wang;Shuo Wang;Joel W. Reid;Jianwen Liang;Jing Luo;Jung Tae Kim;Yang Zhao;Xiaofei Yang;Feipeng Zhao;Weihan Li;Bolin Fu;Xiaoting Lin;Yang Hu;Han Su;Xiaoge Hao;Yingjie Gao;Shutao Zhang;Ziqing Wang;Jue Liu;Hamid Abdolvand;Tsun-Kong Sham;Yifei Mo;Xueliang Sun - 通讯作者:
Xueliang Sun
Safety information on transgenic plants expressing Bacillus thuringiensis-Derived insect control protein
表达苏云金芽孢杆菌衍生昆虫控制蛋白的转基因植物的安全信息
- DOI:
10.1787/oecd_papers-v7-art35-en - 发表时间:
2018 - 期刊:
- 影响因子:9.4
- 作者:
Yunsheng Liu;Yifei Mo - 通讯作者:
Yifei Mo
Superionic conducting vacancy-rich β-Li3N electrolyte for stable cycling of all-solid-state lithium metal batteries
用于全固态锂金属电池稳定循环的富空位超离子导电β-Li3N 电解质
- DOI:
10.1038/s41565-024-01813-z - 发表时间:
2024-11-25 - 期刊:
- 影响因子:34.900
- 作者:
Weihan Li;Minsi Li;Shuo Wang;Po-Hsiu Chien;Jing Luo;Jiamin Fu;Xiaoting Lin;Graham King;Renfei Feng;Jian Wang;Jigang Zhou;Ruying Li;Jue Liu;Yifei Mo;Tsun-Kong Sham;Xueliang Sun - 通讯作者:
Xueliang Sun
Transition of nc-SiC powder surface into grain boundaries during sintering by molecular dynamics simulation and neutron powder diffraction
通过分子动力学模拟和中子粉末衍射研究烧结过程中 nc-SiC 粉末表面向晶界的转变
- DOI:
10.1524/zkri.2007.2007.suppl_26.255 - 发表时间:
2007 - 期刊:
- 影响因子:0
- 作者:
Marcin Wojdyr;Yifei Mo;E. Grzanka;S. Stelmakh;S. Gierlotka;T. Proffen;T. W. Żerda;B. Palosz;I. Szlufarska - 通讯作者:
I. Szlufarska
Association between gestational hypnotic benzodiazepine receptor agonists exposure and adverse pregnancy outcomes: a systematic review and meta-analysis
- DOI:
10.1007/s00737-024-01516-3 - 发表时间:
2024-09-24 - 期刊:
- 影响因子:2.700
- 作者:
Xinyuan Wang;Jun Xu;Yifei Mo;Linrun Wang - 通讯作者:
Linrun Wang
Yifei Mo的其他文献
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{{ truncateString('Yifei Mo', 18)}}的其他基金
Collaborative Research: DMREF: Accelerated Data-Driven Discovery of Ion-Conducting Materials
合作研究:DMREF:加速数据驱动的离子导电材料发现
- 批准号:
2118838 - 财政年份:2021
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: Guiding synthesis of nanoparticles with nanometric phase diagram and in situ X-ray diffraction
合作研究:用纳米相图和原位X射线衍射指导纳米颗粒的合成
- 批准号:
2004837 - 财政年份:2020
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: Integrating Physics and Generative Machine-Learning Models for Inverse Materials Design
合作研究:整合物理和生成机器学习模型进行逆向材料设计
- 批准号:
1940166 - 财政年份:2019
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
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- 批准号:50908014
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相似海外基金
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1663893 - 财政年份:2017
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