Abstract:
Understanding the structural characteristics of amorphous alloys at the atomic scale is crucial for elucidating their unique mechanical, thermal, and magnetic properties. However, the absence of long-range order in these materials poses significant challenges for conventional structural analysis techniques. This work presents a GPU-accelerated software framework designed for high-throughput processing and quantitative analysis of High-Resolution Transmission Electron Microscopy (HRTEM) images to reveal hidden atomic orderliness in amorphous alloys. The proposed system integrates parallelized image preprocessing, processing, atom detection, radius-based clustering, and graph-theoretical and entropy-based metrics to quantify short- and medium-range order. A modular architecture enables efficient GPU computation using CUDA, CuPy, and optimized memory strategies, achieving speedups of up to $220\times$ compared to CPU implementations. Validation was conducted on both simulated datasets (FeB, CoNiFeSiB) and real HRTEM images of amorphous alloys (CoP, NiW, Fe-based 71ÊÍÑÐ). Results demonstrate strong correlations between cluster size, bond angle distributions, and entropy metrics with macroscopic material properties such as hardness and thermal stability. Larger clusters and obtuse bond angles were found to indicate increased local structural order, while entropy measures provided sensitive discrimination of disorder.
