Schweinhart and Berry Receive Funding for Artificial Intelligence Project
Benjamin Schweinhart and Tyrus Berry, Mathematical Sciences, have received funding from the National Science Foundation for the project: “Collaborative Research: EAGER: ADAPT: Charting the Space of Material Microstructures with Artificial Intelligence.” This interdisciplinary research project will be conducted in collaboration with Jeremy Mason, Materials Science & Engineering, UC Davis.
One of the fundamental principles of materials science is that material properties are determined by structure. The microstructure, or the internal structure at the micron scale (one millionth of a meter), is specifically identified as being essential to physical properties including the mechanical strength, ductility, and fracture toughness of ceramic and metal components used in construction, manufacturing, and other industrial applications. Since it is possible and even likely that microstructures of exceptional materials of the future will not resemble those of conventional materials, a key challenge in material development is the determination of the space of feasible microstructures.
This award will support research and education activities that will adapt leading methods in data science and machine learning to address this challenge. Specifically, the research will integrate expert knowledge about physically-meaningful comparisons of microstructures into machine learning models to provide a systematic method for exploring possible microstructures, both previously realized and unrealized ones. This approach is also expected to improve the accuracy and efficiency of models to predict material properties on the basis of microstructure alone.
The collaborators will create opportunities for undergraduate and graduate students in mathematics and materials science to be cross-trained between disciplines and institutions. The mathematics students will benefit from interactions with materials scientists and vice versa. In addition, Mason, Schweinhart, and Berry will create user-friendly software to make the proposed algorithms widely accessible, both to researchers and industrial practitioners and to individuals in other disciplines studying structures with similar geometry.
The project was awarded $299,141 from the NSF, of which $192,372 is allocated for research at George Mason. Funding began in September 2022 and is estimated to end in late August 2024.
