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 Abstract

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.

National Science Foundation (NSF)
Division of Materials Research (DMR)
Standard Grant (Standard)
Application #
Program Officer
John Schlueter
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Arizona State University
United States
Zip Code