Local-distortion-informed exceptional multicomponent transition-metal carbides uncovered by machine learning

Abstract Developing high-performance multicomponent ceramics, which are promising in solving challenges posed by emerging technologies, shows grand difficulties because of the immense compositional space and complex local distortions. In this work, an accurate machine learning (ML) model built upon an ab initio database is developed to predict the mechanical properties and structural distortions of multicomponent transition metal carbides (MTMCs). The compositional space of MTMCs is thoroughly explored by the well-trained model. Combined with electronic and geometrical analysis, we show that the elemental adaptability to the rock-salt structure elegantly elucidates the mechanical characteristics of MTMCs, and such adaptability can be quantified by local lattice distortions. We further establish new design principles for high-strength MTMCs, and V–Nb–Ta-based MTMCs are recommended, which are validated by the present experiments. The proposed model and design philosophy pave a broad avenue for the rational design of MTMCs with exceptional properties.