SI2-SSI: Collaborative Research: A Computational Materials Data and Design Environment

SI2-SSI：协作研究：计算材料数据和设计环境

• 批准号: 1148011
• 负责人: Dane Morgan
• 金额: $ 105万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1148011&HistoricalAwards=false
• 关键词: SI2; SSI; Collaborative; Research; Computational

TECHNICAL SUMMARYThe Office of Cyberinfrastructure, Division of Materials Research, and Chemistry Division contribute funds to this award made on a proposal to the Software Infrastructure for Sustained Innovation solicitation. This award supports development of new theory and tools to enable rapid and efficient calculation of atomic level material properties. The incredible advances in computing power and tools of atomic scale simulation have now made it possible to predict critical properties for existing and new materials without experimental input. However, present simulation approaches typically require researchers to perform many steps by hand, which is both slow and error prone compared to what a computer can do. Through computer codes that automate the tasks in first principles modeling human bottlenecks can be removed and predictive capabilities of first principles simulation techniques can be accelerated by orders of magnitude. Such a high-throughput computing approach will enable generation of critical materials data on an unprecedented scale and open new doors for material science.The team will develop tools for the specific challenges of predicting point defect properties, atomic diffusion, and surface stability, with a focus on automating steps to enable computations on a massive scale. The PIs will use state-of-the-art first principles quantum mechanical methods. Best practices for treating the multiple issues of charged defect calculations, for example convergence with cell size and band gap errors, will be refined and automated for rapid execution. Similarly, tools to identify diffusion pathways and determine their barriers will be streamlined to allow users to quickly identify transport properties of new systems. New theoretical approaches to modeling charged surfaces will be developed to enable simulation of surfaces in more realistic environments. This award will support prediction of properties that play a critical role in advancing a wide range of technologies, from improving semiconductors for next generation computers to better fuel cells for more efficient energy conversion. Software tools and data produced by this effort will enable researchers to predict properties for thousands of materials with almost no human effort, accelerating the pace at which researchers can develop new materials technologies.Software and data developed from this award will be shared with academic and industrial researchers through modules on the web, scientific journals and presentations at national and international conferences. This award supports two workshops to educate researchers about the latest opportunities to use high-throughput computing of atomic scale properties for materials development. Students will be trained to work at the critical interface of the computer and physical sciences, supporting a generation of scientists who use modern computers to their fullest potential to develop new understanding and technology.NON-TECHNICAL SUMMARYThe Office of Cyberinfrastructure, Division of Materials Research, and Chemistry Division contribute funds to this award made on a proposal to the Software Infrastructure for Sustained Innovation solicitation. This award supports development of new theory and tools to enable rapid and efficient calculation of atomic level material properties. The incredible advances in computing power and tools of atomic scale simulation have now made it possible to predict critical properties for existing and new materials without experimental input. However, present simulation approaches typically require researchers to perform many steps by hand, which is both slow and error prone compared to what a computer can do. Through computer codes that automate the tasks in first-principles modeling human bottlenecks can be removed and predictive capabilities of first principles simulation techniques can be accelerated by orders of magnitude. Such a high-throughput computing approach will enable generation of critical materials data on an unprecedented scale and open new doors for material science.The team will develop tools for the specific challenges of predicting point defect properties, atomic diffusion, and surface stability, with a focus on automating steps to enable computations on a massive scale. These properties play a critical role in advancing a wide range of technologies, from improving semiconductors for next generation computers to better fuel cells for more efficient energy conversion. Software tools and data produced by this effort will enable researchers to predict properties for thousands of materials with almost no human effort, accelerating the pace at which researchers can develop new materials technologies.Software and data developed from this award will be shared with academic and industrial researchers through modules on the web, scientific journals and presentations at national and international conferences. In particular, this award will support two workshops to educate researchers about the latest opportunities to use high-throughput computing of atomic scale properties for materials development. This award will train students to work at the critical interface of the computer and physical sciences, supporting a generation of scientists who use modern computers to their fullest potential to develop new understanding and technology.

技术摘要网络基础设施办公室、材料研究部和化学部根据持续创新软件基础设施征集提案为该奖项提供资金。该奖项支持开发新的理论和工具，以实现快速有效地计算原子级材料特性。 计算能力和原子尺度模拟工具的令人难以置信的进步，现在已经可以在没有实验输入的情况下预测现有和新材料的临界特性。 然而，目前的模拟方法通常需要研究人员手动执行许多步骤，与计算机可以做的相比，这既慢又容易出错。 通过计算机代码，自动化的任务，在第一原理建模的人的瓶颈可以被删除和预测能力的第一原理仿真技术可以加快数量级。 这种高通量计算方法将能够以前所未有的规模生成关键材料数据，并为材料科学打开新的大门。该团队将开发工具，以应对预测点缺陷特性、原子扩散和表面稳定性等特定挑战，重点是自动化步骤，以实现大规模计算。 PI将使用最先进的第一原理量子力学方法。 处理带电缺陷计算的多个问题的最佳实践，例如与单元尺寸和带隙误差的收敛，将被细化和自动化以快速执行。 同样，查明扩散途径和确定其障碍的工具将得到简化，使用户能够迅速查明新系统的运输特性。 新的理论方法建模带电表面将开发，使模拟表面在更现实的环境。 该奖项将支持在推进各种技术方面发挥关键作用的性质预测，从改进下一代计算机的半导体到更好的燃料电池，以实现更有效的能量转换。 该奖项所产生的软件工具和数据将使研究人员能够预测数千种材料的特性，几乎不需要人工，从而加快研究人员开发新材料技术的步伐。该奖项所开发的软件和数据将通过网络模块、科学期刊以及在国家和国际会议上的演讲与学术和工业研究人员共享。 该奖项支持两个研讨会，以教育研究人员利用高通量计算原子尺度特性进行材料开发的最新机会。 学生将接受培训，在计算机和物理科学的关键接口工作，支持一代科学家谁使用现代计算机，以最大限度地发挥其潜力，以发展新的理解和技术。非技术性总结网络基础设施办公室，材料研究部和化学部提供资金，这个奖项的建议，以软件基础设施的持续创新征集。该奖项支持开发新的理论和工具，以实现快速有效地计算原子级材料特性。 计算能力和原子尺度模拟工具的令人难以置信的进步，现在已经可以在没有实验输入的情况下预测现有和新材料的临界特性。 然而，目前的模拟方法通常需要研究人员手动执行许多步骤，与计算机可以做的相比，这既慢又容易出错。 通过计算机代码，自动化的任务，在第一原理建模的人的瓶颈可以被删除和预测能力的第一原理仿真技术可以加快数量级。 这种高通量计算方法将能够以前所未有的规模生成关键材料数据，并为材料科学打开新的大门。该团队将开发工具，以应对预测点缺陷特性、原子扩散和表面稳定性等特定挑战，重点是自动化步骤，以实现大规模计算。 这些特性在推进各种技术方面发挥着关键作用，从改进下一代计算机的半导体到更好的燃料电池，以实现更有效的能量转换。 该奖项所产生的软件工具和数据将使研究人员能够预测数千种材料的特性，几乎不需要人工，从而加快研究人员开发新材料技术的步伐。该奖项所开发的软件和数据将通过网络模块、科学期刊以及在国家和国际会议上的演讲与学术和工业研究人员共享。 特别是，该奖项将支持两个研讨会，以教育研究人员利用高通量计算原子尺度特性进行材料开发的最新机会。 该奖项将培养学生在计算机和物理科学的关键接口工作，支持一代科学家利用现代计算机充分发挥其潜力，以发展新的理解和技术。

项目成果

Prediction of concrete coefficient of thermal expansion and other properties using machine learning

• DOI: 10.1016/j.conbuildmat.2019.05.006
• 发表时间: 2019-09
• 期刊: Construction and Building Materials
• 影响因子: 7.4
• 作者: V. Nilsen;Le T. Pham;Michael Hibbard;Adam Klager;S. Cramer;D. Morgan

Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures

• DOI: 10.1088/1367-2630/16/1/015018
• 发表时间: 2014-01-13
• 期刊: NEW JOURNAL OF PHYSICS
• 影响因子: 3.3
• 作者: Angsten, Thomas;Mayeshiba, Tam;Morgan, Dane
• 通讯作者: Morgan, Dane