A thorough descriptor search to machine learn the lattice thermal conductivity of half-Heusler alloys

Predicting the lattice thermal conductivity (K_L) of compounds prior to synthesis is an extremely challenging task because of the complexity associated with determining the phonon scattering lifetimes for underlying normal and Umklapp processes. An accurate ab-initio prediction is extremely expensive computationally, seeking data-driven alternatives. We perform machine learning (ML) on theoretically computed K_L of half-Heusler (HH) compounds. An exhaustive descriptor list comprising of elemental and compound descriptors is used to build several ML models. We find that ML models built with compound descriptors can reach high accuracy with a fewer number of descriptors, while a set of a large number of elemental descriptors may be used to tune the performance of the model as accurately. Thereby, using only the elemental descriptors, we build a model with exceptionally high accuracy (with an R² score of ~0.95) using one of the compress sensing techniques. This work, while unfolding the complex interplay of the descriptors in different dimensions, reveals the competence of the simple low-level elemental descriptors in building a robust model for predicting the K_L.