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Low lattice thermal conductivity is essential for high thermoelectricperformance of a material. Lattice thermal conductivity is often computed usingdensity functional theory (DFT), typically at a high computational cost.Training machine learning models to predict lattice thermal conductivity couldoffer an effective procedure to identify low lattice thermal conductivitycompounds. However, in doing so, we must face the fact that such compounds canbe quite rare and distinct from those in a typical training set. Thisdistinctness can be problematic as standard machine learning methods areinaccurate when predicting properties of compounds with features differingsignificantly from those in the training set. By computing the lattice thermalconductivity of 122 half-Heusler compounds, using the temperature-dependenteffective potential method, we generate a data set to explore this issue. Wefirst show how random forest regression can fail to identify low latticethermal conductivity compounds with random selection of training data. Next, weshow how active selection of training data using feature and principalcomponent analysis can be used to improve model performance and the ability toidentify low lattice thermal conductivity compounds. Lastly, we find thatactive learning without the use of DFT-based features can be viable as aquicker way of selecting samples.

Lattice thermal conductivity of half-Heuslers with density functional  theory and machine learning: Enhancing predictivity by active sampling with  principal component analysis