CDS&E: Inferring Lattice Dynamics from Temporal X-ray Diffraction Data

University Of Rochester, Rochester NY

Investigators

Abstract

NONTECHNICAL SUMMARY Emerging X-ray scattering experimental techniques provide the capability to probe correlations between atomic structure and physical properties of materials with atomic-scale (around one-billionth of a meter) sensitivity and with one trillionth of a second time resolution. The images obtained in these scattering experiments contain visual features, such as rings, spots, and halos, which encode detailed information about the atomic structure and its time evolution. However, the corresponding data sets are enormously large, and therefore, manually analyzing them with many uncertainties cannot be solely performed by human experts. This award supports research and educational activities to develop artificial intelligence techniques to mine data from X-ray scattering experiments to detect atomic-scale mechanisms of phase transformations and plastic deformation in materials when they are subjected to extreme conditions like high pressure, temperature or strain. Achieving a fundamental understanding of the mechanisms that govern the arrangement and motion of atoms is crucial for identifying new pathways of forming new matter with desired properties and behavior at extreme conditions. This project will also provide multidisciplinary training for undergraduate and graduate students in computational materials science, advanced atomic-level structural/chemical characterization, molecular dynamics simulations, and artificial intelligence techniques. The project will inform the design of new course material and modules on artificial intelligence applied to materials science. The PIs will also be involved in outreach to K-12 students aimed at broadening participation in science, technology, engineering, and mathematics and raise awareness of nanotechnology and materials science. TECHNICAL SUMMARY This award supports research and educational activities aimed at developing automated deep-learning computer vision techniques to mine x-ray diffraction (XRD) data to identify crystal structures and detect lattice-level mechanisms responsible for phase transformation and plastic deformation under extreme conditions. At very high pressures, temperatures, or strain rates when lattice variations or occurrence of new phases are not known a priori, analyzing vast datasets of snapshots from billions of XRD measurements become inaccurate, or fail completely. To overcome this challenge, the research team will leverage a series of novel and advanced techniques, including multimodal fusion, reconstruction, space-time modeling, weak supervision, domain adaptation, and visualization to achieve the following objectives: 1) Generation of static and temporal synthetic one-dimensional XRD patterns and two-dimensional XRD images, 2) Development of deep learning models for static and temporal classification of crystal structures, 3) Development of interpretation techniques for explanation and justification of deep learning models and predictions, and 4) Domain adaptation to large experimental data. The successful development of such deep learning techniques will lead to deeper understanding of unknown phenomena in materials under extreme conditions when no prior knowledge is available. This project will also provide multidisciplinary training for undergraduate and graduate students in computational materials science, advanced atomic-level structural/chemical characterization, molecular dynamics simulations, and deep learning techniques. The project will inform the design of new course material and modules on applied deep learning for materials science. The PIs will also be involved in outreach to K-12 students aimed at broadening participation in science, technology, engineering, and mathematics and raise awareness of nanotechnology and materials science 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.