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

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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.