This work presents a method for the sonification of X-ray diffraction (XRD) patterns based on physically grounded relationships. The proposed approach transforms experimental diffraction data directly into an audio signal, avoiding arbitrary mappings and subjective peak selection.For each point of the diffraction pattern, the angular information is converted into structural parameters through Bragg’s law and expressed in reciprocal space. These values are then mapped to the audible domain through a linear normalization of the scattering vector, preserving the relative structure of the original dataset while maintaining a monotonic correspondence with the underlying physical variable.The diffracted intensity is transformed into sound amplitude using a square-root relationship, consistent with the physical connection between intensity and wave amplitude. A global amplitude scaling is introduced, avoiding dataset-dependent normalization and preserving relative intensity differences across different materials.The final audio signal is constructed as an ordered temporal sequence of elementary sinusoidal contributions associated with all points of the diffraction pattern.The method is applied to mineralogical datasets obtained from the RRUFF database, including calcite, aragonite, diamond, and graphite. In addition to individual case studies, comparative analyses between polymorphic systems are performed in order to evaluate whether the proposed transformation preserves crystallographic differences independently of chemical composition.The results show that the sonified signals preserve the main structural features of the original diffraction patterns. Comparisons between calcite and aragonite, and between diamond and graphite, further indicate that the method is sensitive to crystallographic organization rather than chemical composition alone, supporting the feasibility of a physically grounded framework for the sonification of crystallographic data.These findings support the validity of a physically constrained approach to data sonification, in which acoustic representations retain the structural information of crystallographic datasets.
Angelo Diano (Tue,) studied this question.