Singing Materials: Initial experiments in applying sonification to phonon spectra

TL;DR

This paper introduces SingingMaterials, a Python package that applies sonification to phonon spectra data, enabling auditory exploration of vibrational material properties.

Contribution

It presents a modular, extensible software tool that supports multiple sonification methods for phonon data, integrating with the Materials Project database.

Findings

Listeners can distinguish material property differences through sonified data

Sonification offers an interpretable, complementary approach to visual data analysis

The software supports spectral, synthesised, and sample-based sonification methods

Abstract

Solid materials may appear static, but at the atomic scale they are in constant vibrational motion. These vibrations, described by phonons, govern many key material properties, including structural stability, mechanical strength, optical behaviour, and thermal transport. Understanding phonon physics is therefore central to the rational design of materials with targeted functionalities. Singing Materials is a research project that explores how sonification can be applied to this domain. In this work, we introduce \texttt{SingingMaterials}, a modular Python package for sonifying materials simulation data. The software interfaces with the Materials Project database and is designed to be extensible, enabling the incorporation of additional sonification strategies and data sources. Built using the Sonification Toolkit \texttt{Strauss}, the current implementation supports three core approaches: spectral, synthesised, and sample-based. We demonstrate these approaches using phonon density-of-states data and evaluate their effectiveness through a user study, investigating whether listeners can distinguish differences in material properties from their auditory representations. The results show that sonification can provide an interpretable and complementary approach for exploring vibrational materials data.