From density functional theory to machine learning predictive models for electrical properties of spinel oxides

HIGHLIGHTS

• What: The authors compare five machine_learning algorithms: kernel ridge regression (KRR), support vector regression (SVR), random forests (RF), neural_networks (NN), and ensemble of all algorithms. The dataset for this model consists of two band structures (one for each spin) from each of the 190 compositions that were calculated, resulting in a total of 380 samples of band structures. This model demonstrates the ability to predict the physical property of conductivity based on electronic structure inputs such as bandwidths. The aim of this research was to investigate the conductivity properties of spinel oxide materials used in electrochemical applications . . .
  

   

  

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