Global structure optimization and metastable structure enumeration using polynomial machine learning potentials

Machine learning potentials (MLPs) have become indispensable tools for performing efficient and accurate large-scale atomistic simulations and crystal structure predictions. The polynomial MLPs described by polynomial rotational invariants have also been systematically developed for many elemental and alloy systems, and they are available in the Polynomial Machine Learning Repository [1]. We show a procedure for performing structure enumerations, including global structure optimization, accelerated by the polynomial MLPs. The polynomial MLPs are developed from datasets generated from various structures and are accurate for many local minimum structures. However, the MLPs exhibit some ghost local minimum structures and fail to predict the energy for some local minimum structures accurately. Therefore, we iteratively repeat random structure searches and update the MLPs using the density functional theory datasets where structures predicted to be local minima are appended. The current procedure is systematically applied to structure enumerations in the elemental systems of As, Bi, Ga, In, La, P, Sb, Sn, and Te with many complex metastable structures [2] and the alloy systems of Al-Cu [3] and Cu-Ag-Au systems. The current procedure would accelerate global structure searches and expand their search space significantly.



[1] A. Seko, J. Appl. Phys. 133, 011101 (2023), https://sekocha.github.io

[2] A. Seko, in preparation.

[3] H. Wakai, A. Seko, and I. Tanaka, J. Ceram. Soc. Jpn., 131, 762 (2023).