Exploring thermal properties of PbSnTeSe and PbSnTeS high entropy alloys with machine-learned potentials

Abstract Lattice thermal conductivity plays an important role in material science, especially significant in thermoelectric materials. Recent research has unveiled the potential of high entropy alloys (HEAs) as good candidates for thermoelectric materials due to their notably low lattice thermal conductivity. This study investigates the lattice thermal conductivities of two specific HEAs, namely PbSnTeSe and PbSnTeS, through the application of molecular dynamics simulations (MDS) with machine-learned potentials. The findings not only demonstrate substantial agreement with experimental results for PbSnTeSe but also highlight the precision and efficiency of machine-learned potentials as a powerful tool in material research. The combination of machine-learned potentials with classical MDS offers an effective solution for simulating the thermodynamic properties of complicated systems like HEAs, with accuracy comparable to first-principle calculations. Furthermore, the investigation reveals that the lattice thermal conductivities of PbSnTeS are lower than those of PbSnTeSe, indicating its potential as a promising candidate for thermoelectric materials.