The Kinetics of Ni/Al Reactive Intermetallic Composites

Molecular dynamics (MD) simulations have been used to study the underlying physics and atomistic mechanisms of the reaction progression in Ni/Al reactive intermetallic composites. Preparation of these composites, either through deposition techniques or through the process of mechanical ball milling, gives rise to a periodic ordered, nanolaminated structure and in the first part of this thesis, the effects of this laminate period, ignition temperature and volumetric defects are studied. The presence of defects not only speeds up the reaction by as much as 5 times, but changes the nature of mass transport from diffusive to partly ballistic. Subsequently, the feasibility of using amorphous energetic materials is studied. The use of amorphous precursors is found to speed up the reaction as well as increase the heat of reaction, starting as it does from a higher energy state. Amorphous Ni recrystallizes at elevated temperatures and this process has been investigated (both thermal and shock induced recrystallization). The results presented herein, hint at the possibility of nanostructural tiling and the building of hierarchal nanostructures, starting from amorphous rather than liquid or chemical precursors

AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources

We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography. Ptychography is a lensless method that is being used to image samples through a simultaneous numerical inversion of a large number of diffraction patterns from adjacent overlapping scan positions. This acquisition method can enable nanoscale imaging with x-rays and electrons, but this often requires very large experimental datasets and commensurately high turnaround times, which can limit experimental capabilities such as real-time experimental steering and low-latency monitoring. In this work, we introduce a software system that can automate ptychography data analysis tasks. We accelerate the data analysis pipeline by using a modified version of PtychoNN -- an ML-based approach to solve phase retrieval problem that shows two orders of magnitude speedup compared to traditional iterative methods. Further, our system coordinates and overlaps different data analysis tasks to minimize synchronization overhead between different stages of the workflow. We evaluate our workflow system with real-world experimental workloads from the 26ID beamline at Advanced Photon Source and ThetaGPU cluster at Argonne Leadership Computing Resources.Comment: 7 pages, 1 figure, to be published in High Performance Computing for Imaging Conference, Electronic Imaging (HPCI 2023