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. 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’. Data from these characterization studies also serve as inputs to material models, which are in turn are used to make predictions at spatio-temporal scales inaccessible to the experiment. 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 our work in the development of a machine learning framework that enables rapid and accurate development of material models applicable to a wide variety of materials properties including thermal transport.

Bio: Mathew Cherukara is a post-doc at the Advanced Photon Source at Argonne National Laboratory. He received his Ph.D in Materials Engineering from Purdue University in 2015 and his bachelor’s from the Indian Institute of Technology (IIT Madras) in 2010. His background is in massively-parallel reactive molecular dynamics (MD) simulations and machine learning techniques applied to predictive materials modeling. He now performs coherent diffraction imaging (CDI) experiments in combination with computational modeling to study dynamic processes at slow and ultra-fast (sub-ns) timescales. He is the recipient of research awards from the Materials Research Society (MRS), Defense Threat Reduction Agency (DTRA) and the College of Engineering at Purdue.