Formation ability descriptors for high-entropy diborides established through high-throughput experiments and machine learning

Establishing formation ability descriptors is essential for facilitating the discovery and design of high-entropy diborides (HEBs) with tailorable properties. In this work, we establish an efficient combination of formation ability descriptors of HEBs through the high-throughput synthesis and calculations combined with the machine learning approach. Firstly, 70 HEB samples are synthesized by the self-developed high-throughput combustion synthesis apparatus, and 22 formation ability descriptors are computed simultaneously. According to these results, four formation ability descriptors, namely Pauling electronegativity difference ( δ χ P ), average volume, average bulk modulus, and density difference, are chosen and proposed as the optimal combination of HEBs with 97.1% validation accuracy through Pearson correlation coefficient analysis and machine learning method. In particular, the δ χ P is the most significant feature among them and the smaller δ χ P is, the more accessible synthesis of HEBs is. This study is expected to facilitate the discovery and design of HEBs with tailorable properties.