Machine Learning Lattice Parameters of M₂AX Phases

MAX phases, with a general composition of Mn+1AXn, are layered materials with hexagonal symmetry that have increasingly captivated a lot of attention because of their unique way of combining ceramic and metallic properties into a homogeneous bulk material. We developed a machine learning approach to predict the lattice parameters a and c of M2AX phases. This approach consists of training an ensemble model on a data set collecting all experimentally synthesized M2AX phases’ lattice parameters. Our approach combines a data augmentation scheme with state-of-the-art regression models and hyperparameter optimization tools. We tested our model on newly synthesized compositionally complex high-entropy M2AX phases with positive results. Finally, we also show that our machine learning predictions of lattice parameters are useful as initial values for variable-cell relaxations of M2AX structures with the density functional theory.