Collaborative Research: EAGER: ADAPT: Charting the Space of Material Microstructures with Artificial Intelligence

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NONTECHNICAL SUMMARYOne 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 all 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. This award 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, the PIs 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.TECHNICAL SUMMARYThis award supports the development of a new representation of microstructure state space that balances the need to retain enough information to predict physical properties of materials with the requirement that it be sufficiently low-dimensional and general to serve as the basis for a flexible materials database. The concept of computational materials design relies on the underlying ideas that (i) a microstructure can be represented as a point in an appropriate state space, (ii) this state space specifies enough information to accurately predict material properties, and (iii) optimization routines could be used to search the state space for microstructures with desirable properties. In this research program, the PIs will adapt leading methods in data science and machine learning to discover a practicable representation of this microstructure space applicable to a variety of material classes. Formally, the feature extraction, classification, and interpretability of experimental microstructure data will be improved by achieving three aims. Aim I: Define and implement physically-motivated metrics to evaluate the similarity of microstructures on both local and global scales. Aim II: Leverage the local metric with manifold learning to construct a coordinate representation for the space of windows, and apply these coordinates in conjunction with new machine learning techniques to to predict material properties. Aim III: Learn a coordinate representation for the space of window distributions and use it to construct a proof-of-concept microstructure database.This award 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, the PIs 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.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.