| NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
| Recipient: |
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| Initial Amendment Date: | September 20, 2012 |
| Latest Amendment Date: | September 20, 2012 |
| Award Number: | 1148011 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Alan Sussman
OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2012 |
| End Date: | September 30, 2018 (Estimated) |
| Total Intended Award Amount: | $1,050,000.00 |
| Total Awarded Amount to Date: | $1,050,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
21 N PARK ST STE 6301 MADISON WI US 53715-1218 (608)262-3822 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
21 North Park Street Madison WI US 53715-1215 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
OFFICE OF MULTIDISCIPLINARY AC, DMR SHORT TERM SUPPORT, CHEMISTRY PROJECTS, Software Institutes |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
TECHNICAL SUMMARY
The 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 SUMMARY
The 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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project involved collaborations from 4 institutions: University of Wisconsin - Madison, Massachusetts Institute of Technology, Lawrence Berkeley Laboratory, and the University of Kentucky.
Computational materials design is a transformative technology for the identification and optimization of new functional materials. First-principles calculations based on quantum mechanics enable new materials to be predicted, screened for desired properties, and downselected by functionality, largely in the absence of any new experimental data. This work has centered on developing new methods, tools and cyber infrastructure to accelerate first-principles materials design. Our particularly focus has been on defects and diffusion, two closely coupled materials properties which play a critical role in many materials applications, from efficient solar cells to fast charging batteries.
New cyberinfrastructure developed in this work consisted of primarily of workflow/data analysis and data management tools. The MAterials Simulation Toolkit (MAST)[1] was developed as an open source project and allows users to automatically run and analyze thousands of first-principles and analysis calculations in complex workflows for determining materials properties, particularly those related to defects and diffusion in crystals. The toolkit has been extended to MAST-Machine Learning (MAST-ML)[2], which automates the application of machine learning tools to materials problems (Figure 1). Additional tools supporting different aspects of materials analysis are available as online apps on the MaterialsHub[3]. Taken together, these tools enable researchers to more rapidly generate first-principles data and build machine learning models for materials discovery and optimization. This project also helped support the development of the contribution framework MPContribs[4] in the Materials Project, built for sharing materials data and web applications. This work has supported a joint effort between the teams to develop a sustainable pipeline for the automatic processing, upload and dissemination of data to the Materials Project databases. Data from this project are now user-contributed datasets[5] in the Materials Project accessible through the Materials Project REST API. The Materials Project now provides a dissemination vehicle for project data and thus solves the problem of sustainability in hosting it online (Figure 2).
This project also developed new theoretical methods that supported and enhanced the cyberinfrastructure. We developed two approaches to improve scalable implementation of first-principles materials design. First, we identified a metric for rigorously evaluating the similarity of crystal structures, defining a notion of structural and compositional distance which may be used in classifying and data mining structurally-determined material properties. Second, we proposed an efficient method for identifying cationic migration geometries and energies, enabling the high-throughput evaluation of properties controlled by ionic migration (Figure 3).
We also developed new approaches to help researchers synthesize predicted materials. Specifically, we proposed that it is possible to predict the conditions under which a target structure may be synthesized by evaluating the instantaneous driving forces for phase formation. These driving forces may be evaluated through efficiently computable, quasi-thermodynamic models, within a chosen synthesis method. We developed one such model for evaluating the thermochemistry of aqueous mineral interfaces, accounting for the effects of solvation and ionic adsorption, as these interfaces play a crucial role in guiding the nucleation and growth of solids from solution. We used this approach to evaluate how the acidity of a growth medium affects structure selection in the crystallization of FeS2, and found results in good agreement with known experiments (Figure 4). As a broader test of this vision, we evaluated the effect of off-stoichiometric intermediates in determining structure selection in MnO2 polymorphs, which is another abundant and technologically relevant mineral system.
Finally, we have applied the tools developed in this work in materials design research, both to demonstrate the tools to the community and as test cases to optimize the approaches. Results include
- The largest database of dilute impurity diffusion coefficients from any single group, with over 400 systems calculated to date, hosted for convenient search and with missing data predicted by machine learning tools (Figure 5)[6].
- Prediction of novel catalysts for solid oxide fuel cells (Figure 6).
- Prediction and new understanding of strain effects on defect formation and migration in fast oxygen diffusers used in fuel cells, gas separation, and other applications.
- New mechanisms of interstitial diffusion in fast oxygen diffusers.
- Identification of property degrading defects in GaAsBi semiconductors for optical, electronic, and spintronic devices.
[1] https://pythonhosted.org/MAST
[2] https://github.com/uw-cmg/MAST-ML
[3] https://nanohub.org/groups/materialshub
[4] https://materialsproject.org/mpcontribs
[5] Dilute Solute Diffusion: https://materialsproject.org/mpcontribs/dilute_solute_diffusion/; MnO2 Phases: https://materialsproject.org/mpcontribs/MnO2_phase_selection/; Perovskites Diffusion: https://materialsproject.org/mpcontribs/perovskites_diffusion/
[6] https://matmodapp.engr.wisc.edu/https-only/dsd_calculated/cmg_dilute_solute_diffusion_main.php
Last Modified: 12/28/2018
Modified by: Dane Morgan
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