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

技术总结网络基础设施办公室、材料研究司和化学司为该奖项提供资金，该奖项是根据向软件基础设施持续创新征集提出的建议而设立的。该奖项支持开发新的理论和工具，以便能够快速有效地计算原子级材料的性质。计算能力和原子尺度模拟工具的惊人进步现在使预测现有材料和新材料的关键性质成为可能，而不需要实验输入。然而，目前的模拟方法通常需要研究人员手工执行许多步骤，与计算机可以做的相比，这既慢又容易出错。通过将第一原理建模中的任务自动化的计算机代码，可以消除人类的瓶颈，第一原理模拟技术的预测能力可以加速几个数量级。这种高吞吐量的计算方法将使关键材料数据的生成达到前所未有的规模，并为材料科学打开新的大门。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等具体挑战，重点是自动化步骤，使大规模计算成为可能。PI将使用最先进的第一原理量子力学方法。将改进和自动化处理收费缺陷计算的多个问题的最佳做法，例如与单元大小和带隙误差的收敛，以便快速执行。同样，将简化确定扩散路径和确定其障碍的工具，使用户能够快速确定新系统的运输特性。将开发新的理论方法来模拟带电表面，以便能够在更真实的环境中模拟表面。该奖项将支持对在推动一系列技术进步方面发挥关键作用的性能的预测，从为下一代计算机改进半导体到更好的燃料电池以实现更高效的能量转换。这项工作产生的软件工具和数据将使研究人员能够几乎不费人力就能预测数千种材料的性质，加快研究人员开发新材料技术的步伐。从该奖项开发的软件和数据将通过网络上的模块、科学期刊和在国内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，以教育研究人员使用原子尺度属性的高通量计算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界面上工作，支持使用现代计算机的一代科学家最大限度地开发新的理解和技术。非技术总结网络基础设施办公室、材料研究部和化学处为该奖项提供资金，该奖项是根据向软件基础设施持续创新征集提出的建议而设立的。该奖项支持开发新的理论和工具，以便能够快速有效地计算原子级材料的性质。计算能力和原子尺度模拟工具的惊人进步现在使预测现有材料和新材料的关键性质成为可能，而不需要实验输入。然而，目前的模拟方法通常需要研究人员手工执行许多步骤，与计算机可以做的相比，这既慢又容易出错。通过将第一原理建模中的任务自动化的计算机代码，可以消除人类的瓶颈，第一原理模拟技术的预测能力可以加速几个数量级。这种高吞吐量的计算方法将使关键材料数据的生成达到前所未有的规模，并为材料科学打开新的大门。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等具体挑战，重点是自动化步骤，使大规模计算成为可能。从为下一代计算机改进半导体到更好的燃料电池以实现更高效的能量转换，这些特性在推动一系列技术的发展中发挥着关键作用。这项工作产生的软件工具和数据将使研究人员能够几乎不费人力就能预测数千种材料的性质，加快研究人员开发新材料技术的步伐。从该奖项开发的软件和数据将通过网络上的模块、科学期刊和在国内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，以教育研究人员使用原子尺度属性的高通量计算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界面上工作，支持一代科学家充分利用现代计算机来开发新的理解和技术。

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.