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Using Invariants and Metric Data Structures for Better Materials Research

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Designing representations of materials that are both accurate and computationally tractable at scale is a challenging problem. Typical representations, such as graphs or arrays of structural and compositional descriptors, are inadequate because they are either incomplete or discontinuous under small perturbations. In this talk, I will introduce periodic point sets with equivalence under isometry as models for finite and infinite atomic structures. With this representation choice, structural and chemical isometry invariants provide metric measures of similarity between all atomic structures. The invariants can also compensate for incomplete representations and aid in studying perturbations. After demonstrating the invariants and their relevant properties, I will describe the metric data structures and indexing method we now use and how they have improved our research workflow

This talk is part of the Data Intensive Science Seminar Series series.

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