TitleOptimization of prediction model for elastic constants of high entropy alloys by using LIDG method

We make a prediction model for elastic constants of high-entropy alloys(HEAs) and interpret chemical trend from the model. HEAs are attractive material because of various features emerging by combinations of elements. However, a number of combinations is too large, so exhaustive search by experiment may be impossible. In this presentation, we propose predicting model for HEAs’ elastic property based on density functional theory calculations and machine learning. Elastic constants were calculated for randomly sampled BCC equi-atomic quinary HEAs composed from 25 transition metals by using full-potential Korringa-Kohn-Rostoker coherent potential approximation method. Then, linear regression was performed on calculated data of bulk modulus, c’, and c₄₄. The descriptors of the regression were generated by linearly independent descriptor generation (LIDG) method from arithmetic means and standard deviations of the components features such as three independent elastic constants, lattice constant, group and period of elements, atomic number and electron density parameter r_s. In addition, we optimized the combination of descriptors by the genetic algorithm. We achieved prediction errors of 12.1 GPa for bulk modulus, 5.0 GPa for c’, and 2.2 GPa for c₄₄ which were comparable to the ones generated by the neural networks model. Based on the model, we discuss chemical trend of elastic constants.