| NSF Org: |
DMR Division Of Materials Research |
| Recipient: |
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| Initial Amendment Date: | August 25, 2020 |
| Latest Amendment Date: | March 6, 2023 |
| Award Number: | 2020277 |
| Award Instrument: | Standard Grant |
| Program Manager: |
John Schlueter
jschluet@nsf.gov (703)292-7766 DMR Division Of Materials Research MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | September 1, 2020 |
| End Date: | August 31, 2023 (Estimated) |
| Total Intended Award Amount: | $500,000.00 |
| Total Awarded Amount to Date: | $500,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
660 S MILL AVENUE STE 204 TEMPE AZ US 85281-3670 (480)965-5479 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
P.O. Box 876011 Tempe AZ US 85287-6011 |
| 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): | AI Research 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): | 47.049 |
ABSTRACT
Non-technical Description: High-fidelity predictive modeling of complex materials under extreme conditions (high temperature, high stress, corrosive environment etc.) is crucial for accelerating material design and optimization to address the pressing challenges in our world. This project will aim to leverage both fundamental and use-inspired artificial intelligence (AI) research, coupled with cutting-edge experiments, to revolutionize and transform traditional materials science and engineering (MSE). The novel approach, rooted in the fundamental principle in MSE, that microstructure controls properties, focuses on the development of novel neural architectures that naturally capture the physical causal relations across key microstructural features at multiple length and time scales for predictive modeling and optimal material design. The methodologies and experimental frameworks for constructing novel physics-based learning models developed in this project will be applied to a variety of compelling problems in complex material systems including ceramics, metals and metallic alloys, composites, and porous materials. It is expected that this project will impact many areas including aerospace, microelectronics, petroleum industry, and consumer products.
Technical Description: The theme of this institute involves the development of revolutionary approaches enabled by fundamental and use-inspired AI research, coupled with 4D X-ray microtomography and correlative microscopy, to develop and understand structure-property relationships in vastly different materials systems for both predictively modeling and optimal material design. The goal of the institute will be to accelerate converging research on new learning theories, experimentation methodologies, and validation protocols that will facilitate scientific modeling of the evolutionary and hierarchical structure-property mappings of complex materials systems. In this planning project, researchers mathematically formulate the ubiquitous challenges in modeling complex material structure-property mappings across critical application domains (metals and metallic alloys, multi-functional composites, porous geo-materials, nuclear fuels, etc.), demonstrate the necessity and preliminary feasibility of machine learning and AI in addressing these challenges, and correlate with 4D experiments through x-ray microtomography and correlative microscopy. A consortium of industrial collaborators will be developed to transfer the fundamental knowledge from this program knowledge into practical solutions and to educate a new class of skilled practitioners in the workforce. This project will inspire one to re-think the utility of machine learning in materials science: From knowledge-agnostic feature learning to reasoning mechanisms adaptive to domain-specific knowledge. It will provide the key infrastructure for potential automated materials characterization, research, and discovery.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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.
In this project, we mathematically formulated the ubiquitous challenges in modeling complex material structure-property mappings under the theme of “4D Materials”, across critical application domains (including metals and metallic alloys, multi-functional composites, porous geo-materials, nuclear fuels), demonstrated the necessity and preliminary feasibility of machine learning and AI in addressing these challenges, and identified a consortium of industry collaborators that will create values by transferring the developed knowledge into practical solutions and creating new workforce.
Progresses in three research thrusts have been made: (i) AI-assisted processing and segmentation of multi-model material imaging data; (ii) multi-scale explainable quantitative microstructure representation framework based on graph-neural network and spatial correlation functions; and (iii) multi-scale dynamic neural graph and neural operators for microstructural evolution modeling. A solid infrastructure for carrying out the aforementioned collaborative research is established across the five participant institutions including ASU, Purdue, Syracuse, U. Wyoming, U. Kentucky and UNR. The collaborative research efforts have led to more than a dozen research publications and presentations. Tested codes and documentations have been made publicly available via the team’s github. Collaborations with LLNL and industrial partners including Intel, Uniformity Labs and ALPEMI have been initiated.
A focused workshop on “AI for 4D Materials” was organized including both plenary talks and panel discussions. The participants (100+) of the workshop including researchers from universities, industries and national labs, as well as many postdoc and graduate students. A year-long online seminar/lecture series on AI materials were organized and the recordings were made publicly available on Youtube channel. Research opportunities based on the project have been created for undergraduate students, and summer research internship has been created for high school students, targeting local high schools and under-represented/minority students. Graduate research assistant positions have been created to recruit PhD students from materials, mechanical engineering, physics and computer science to develop next generation of workforce in AI-MSE.
Last Modified: 09/19/2023
Modified by: Yang Jiao
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