THE OBJECTIVE OF THIS RESEARCH IS TO DEVELOP AUTOMATED DEEP-LEARNING-POWERED COMPUTER VISION TECHNIQUES TO MINE INFORMATION-RICH X-RAY DIFFRACTION DATA TO IDENTIFY CRYSTAL STRUCTURES, FILTER, AND DETECT LATTICE-LEVEL MECHANISMS RESPONSIBLE FOR PHASE TRANSFORMATION AND PLASTIC DEFORMATION UNDER EXTREME CONDITIONS. THE UNIVERSITY OF ROCHESTER PROPOSES A DEEP-LEARNING APPROACH TO CLASSIFY CRYSTAL STRUCTURE AND CAPTURE CRYSTAL STRUCTURE EVOLUTION DURING PLASTIC DEFORMATION FROM LIMITED SYNTHETIC XRD PATTERNS. THEY OVERCOME THE SCARCE DATA PROBLEM BY COUPLING A SUPERVISED MACHINE LEARNING APPROACH WITH A PHYSICS-INFORMED DATA AUGMENTATION STRATEGY USING SIMULATED DATA FROM THE INORGANIC CRYSTAL STRUCTURE DATABASE AND ATOMISTIC SIMULATION DATA. THEY WILL CAREFULLY DESIGN THE METHOD TO CAPTURE THE TEMPORAL EVOLUTION OF CRYSTAL STRUCTURE WITHIN A SERIES OF TIME-RESOLVED XRD DATA UNDER EXTREME CONDITIONS OF HIGH PRESSURE, TEMPERATURE, AND STRAIN RATES. THEIR IMAGING TECHNIQUE CAN RAPIDLY CAPTURE THE PLASTIC DEFORMATION EVENTS AMONG LARGE AMOUNTS OF DATA AND LEARN THE NATURE (E.G., DEFECT GENERATION OR NUCLEATION OF NEW PHASES, MELTING, ETC.) OF SUCH BEHAVIOR. FURTHERMORE, THEY WILL DEVELOP EXPLAINABLE DEEP LEARNING TOOLS, INCLUDING CLASS ACTIVATION MAPS AND RELATIONAL REASONING METHODS, TO ALLOW FOR HIGH MODEL INTERPRETABILITY OF THE FINDINGS, IDENTIFY THE IMPORTANT FEATURES IN INTERNAL OPERATIONS OF NEURAL NETWORKS, AND IMPROVE THE FIDELITY OF THE CLASSIFICATION PROCESS. FINALLY, THEY PROPOSE NOVEL DOMAIN ADAPTATION METHODS FOR GENERALIZING THE MODEL TO REAL EXPERIMENTAL DATA AND USE EXPERIMENTAL PATTERNS FOR KNOWN MATERIALS TO EXAMINE THE DEEP LEARNING APPROACH. EVEN THOUGH THEY TRAIN THE DEEP LEARNING MODELS ON MD SIMULATIONS, THEY CAN BE READILY AND QUICKLY APPLIED TO HANDLE ACTUAL EXPERIMENTAL DATA GENERATED FROM X-RAY DIFFRACTION EXPERIMENTS. THEIR LEARNED MODEL WITH A SHARED COMMON REPRESENTATION WILL BE READILY APPLICABLE TO ANALYZING VARIOUS MATERIALS. THE TECHNIQUES DEVELOPED DURING THIS PROJECT CAN FURTHER USE THE LEARNED FEATURES FOR OTHER CHARACTERIZATION METHODS SUCH AS ELECTRON BACKSCATTER DIFFRACTION, RAMAN, AND INFRARED SPECTROSCOPY.