Data-Efficient Training of Machine Learning Interatomic Potential for MAX-Phase Synthesizability Prediction

Abstract

High-Entropy MAX phases (HE-MAX) hold the potential to extend the desirable characteristics of the MXene, including high electrical conductivity, enhanced catalytic activity, and low ion diffusion barriers. However, the synthesis of HE-MXenes requires that their precursor HE-MAX phases be stably synthesizable, and experimentally identifying such compositions remains highly challenging. To address this, we developed a machine learning interatomic potential (MLIP) to predict the thermodynamic stability of MAX phases in order to evaluate the synthesizability of HE-MXenes. MAX phases are generally represented by the formula M n+1 AX n , and in this study, a total of 504 structures were generated by combinations of four or five from eight transition metal candidates within the M₂AX and M₃AX₂ structural types. During active learning, these 504 HE-MAX phases were then clustered, and the centroid of each cluster was chosen as a representative composition for fine-tuning the model. The finetuned CHGNet model achieved an mean absolute error of 0.031 eV/atom in the first iteration, which decreased to 0.027 eV/atom by the eighth iteration using only 401 structures outperforming the CHGNet benchmark of 0.029 eV/atom. Additional structural relaxations 2 performed on vacancy-containing MAX phases further validated the model's robustness and generalization capability. In conclusion, the proposed MLIP framework provides an efficient and accurate pathway for screening thermodynamically stable HE-MAX compositions, offering a practical foundation for guiding future experimental synthesis of HE-MXenes.