Machine learning approaches to identify and design low thermal conductivity oxides for thermoelectric applications

Abstract

The search space for new thermoelectric oxides has been limited to the alloys of a few known systems, such as ZnO, SrTiO3, and CaMnO3. Notwithstanding the high power factor, their high thermal conductivity is a roadblock in achieving higher efficiency. In this paper, we apply machine learning (ML) models for discovering novel transition metal oxides with low lattice thermal conductivity ( $ {k}_L $ ). A two-step process is proposed to address the problem of small datasets frequently encountered in material informatics. First, a gradient-boosted tree classifier is learnt to categorize unknown compounds into three categories of $ {k}_L $ : low, medium, and high. In the second step, we fit regression models on the targeted class (i.e., low $ {k}_L $ ) to estimate $ {k}_L $ with an $ {R}^2>0.9 $ . Gradient boosted tree model was also used to identify key material properties influencing classification of $ {k}_L $ , namely lattice energy per atom, atom density, band gap, mass density, and ratio of oxygen by transition metal atoms. Only fundamental materials properties describing the crystal symmetry, compound chemistry, and interatomic bonding were used in the classification process, which can be readily used in the initial phases of materials design. The proposed two-step process addresses the problem of small datasets and improves the predictive accuracy. The ML approach adopted in the present work is generic in nature and can be combined with high-throughput computing for the rapid discovery of new materials for specific applications.

Keywords

• Machine learning
• oxides
• rapid materials discovery
• thermoelectric
• thermal conductivity