ABSTRACT:
Observing the dynamic behavior of materials following ultra-fast excitation can reveal insights into the response of materials under non-equilibrium conditions of pressure, temperature and deformation. Such insights into materials response under non-equilibrium is essential to design novel materials for catalysis, low-dimensional heat management, piezoelectrics, and other energy applications. However, material response under such conditions is challenging to characterize especially at the nano to mesoscopic spatiotemporal scales. Time-resolved coherent diffraction imaging (CDI) is a unique technique that enables three-dimensional imaging of lattice structure and strain on sub-ns timescales. In such a ‘pump-probe’ technique, stroboscopic x-ray ‘probes’ are used to image the transient response of a sample following its excitation by a laser ‘pump’. In this talk I will present some of our recent work on imaging and modeling of phonon transport and lattice dynamics in nanomaterials. I will also describe my work in the use of deep neural networks in accelerating the analysis of and increasing the robustness of image recovery from 3D X-ray diffraction data. Once trained, our deep neural networks are thousands of times faster than traditional phase retrieval algorithms used for image reconstruction from 3D diffraction data.