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: 1147503
• 负责人: Gerbrand Ceder
• 金额: $ 45万
• 依托单位: Massachusetts Institute of Technology
• 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 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147503&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，材料研究部和化学部门为该奖项捐赠了资金，该奖项是针对持续创新招标的软件基础设施提案颁发的。该奖项支持开发新的理论和工具，以实现原子级材料特性的快速有效计算。 现在，计算能力和原子量表模拟工具的不可思议的进步使得无需实验输入即可预测现有材料和新材料的关键特性。 但是，当前的仿真方法通常要求研究人员手动执行许多步骤，与计算机可以做的相比，这既缓慢又容易出错。 通过计算机代码，可以删除建模人类瓶颈的第一原理中的任务，并且可以通过数量级来加速第一原理模拟技术的预测能力。 这种高通量计算方法将使材料科学的前所未有的规模和开放式新门能够生成关键材料数据。该团队将开发工具，以预测点缺陷属性，原子扩散和表面稳定性的具体挑战，重点是自动启用大规模计算的步骤。 PI将使用最先进的第一原理量子机械方法。 处理带电缺陷计算的多个问题的最佳实践，例如与单元格大小和频带隙错误的收敛性，将进行完善并自动化以快速执行。 同样，可以简化识别扩散途径并确定其障碍的工具，以允许用户快速识别新系统的传输属性。 将开发建模带电表面的新理论方法，以在更现实的环境中模拟表面。 该奖项将支持对属性的预测，这些属性在推进广泛的技术方面起着至关重要的作用，从改善下一代计算机的半导体到更好的燃料电池，以提高燃料电池以进行更有效的能量转换。 这项工作产生的软件工具和数据将使研究人员能够通过几乎没有人为努力来预测数千种材料的财产，从而加速了研究人员可以开发新材料技术的步伐。该奖项开发的软件和数据将通过网络，科学期刊和国家和国际国际介绍的模块与学术和工业研究人员共享。 该奖项支持两个研讨会，以教育研究人员有关使用原子量表属性进行高通量计算进行材料开发的最新机会。 学生将接受培训，可以在计算机和物理科学的关键界面上工作，以支持一代科学家使用现代计算机来发展新的理解和技术的最大潜力。没有技术总结，网络基础设施，材料研究部和化学部门为此奖项贡献了该奖项的基金，以促进维持基金的信息。该奖项支持开发新的理论和工具，以实现原子级材料特性的快速有效计算。 现在，计算能力和原子量表模拟工具的不可思议的进步使得无需实验输入即可预测现有材料和新材料的关键特性。 但是，当前的仿真方法通常要求研究人员手动执行许多步骤，与计算机可以做的相比，这既缓慢又容易出错。 通过计算机代码，可以删除第一原理中的任务，并可以删除人类瓶颈，并且可以通过数量级来加速模拟技术的第一原理的预测能力。 这种高通量计算方法将使材料科学的前所未有的规模和开放式新门能够生成关键材料数据。该团队将开发工具，以预测点缺陷属性，原子扩散和表面稳定性的具体挑战，重点是自动启用大规模计算的步骤。 这些特性在推进广泛的技术方面起着至关重要的作用，从改善下一代计算机的半导体到更好的燃料电池，以提高效率转化。 这项工作产生的软件工具和数据将使研究人员能够通过几乎没有人为努力来预测数千种材料的财产，从而加速了研究人员可以开发新材料技术的步伐。该奖项开发的软件和数据将通过网络，科学期刊和国家和国际国际介绍的模块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，以教育研究人员有关使用原子量表属性进行材料开发的高通量计算的最新机会。 该奖项将培训学生在计算机和物理科学的关键界面上工作，并支持一代科学家使用现代计算机来充分发展新的理解和技术。