  Accurate structural analysis is essential to gain physical knowledge and
understanding of atomic-scale processes in materials from atomistic
simulations. However, traditional analysis methods often reach their limits
when applied to crystalline systems with thermal fluctuations, defect-induced
distortions, partial vitrification, etc. In order to enhance the means of
structural analysis, we present a novel descriptor for encoding atomic
environments into 2D images, based on a pixelated representation of graph-like
architecture with weighted edge connections of neighboring atoms. This
descriptor is well adapted for Convolutional Neural Networks and enables
accurate structural analysis at a low computational cost. In this paper, we
showcase a series of applications, including the classification of crystalline
structures in distorted systems, tracking phase transformations up to the
melting temperature, and analyzing liquid-to-amorphous transitions in pure
metals and alloys. This work provides the foundation for robust and efficient
structural analysis in materials science, opening up new possibilities for
studying complex structural processes, which can not be described with
traditional approaches.
