Unravelling lone pair induced bonding effects on thermal conductivity in metal chalcogenides using machine learning potentials

Abstract

Chemical bonding plays a critical role in phonon dynamics and lattice thermal conductivity (κ_L), essential for designing materials with intrinsically low κ_L. Pnictogen chalcogenides (pn–chg) are attractive candidates due to bonding asymmetry caused by stereochemically active lone pairs (SCALPs), which arise from asymmetric ns-np hybridization. However, since not all pn–chg compounds exhibit low κ_L, it is essential to explore additional bonding-related factors beyond SCALP. To address this and overcome the computational cost of high-throughput screening, we present a scalable transferable framework based on a fine-tuned MatterSim model for efficient prediction of κ_L using the Wigner formulation of heat transport. Furthermore, benchmarking and validating with existing MACE, CHGNet and MatterSim uMLIPs predicted κ_L with high-fidelity predicted κ_L. We introduce bonding descriptors that quantify two key contributors to κ_L, namely the SCALP effect, which accounts for approximately 40 percent, and additional bonding and geometric distortions, which contribute around 60 percent. These findings highlight the dominant role of structural factors beyond SCALP in suppressing κ_L. This work demonstrates that the fine-tuned universal MatterSim model serves as a robust and scalable framework for predicting thermal transport. By incorporating advanced bonding descriptors, it enables the accelerated discovery of low- κ_L materials for thermoelectric applications.