Data for "Machine learning-accelerated evolutionary Monte Carlo for rapid phase exploration of compositionally complex materials in reactive environments"

The HEA and HEO data are stored in folder `Co0.25Mo0.45Fe0.1Ni0.1Cu0.1` and `Co0.2Ni0.2Mg0.2Zn0.2Mn0.2Al2O4`, respectively. In each folder, there are 3 sub-folders: `models` contains the DFT dataset (in `.xyz` format) for training the MACE MLIP, and the trained MACE MLIP `.model` file; `bulk` contains Python scripts for generating initial population, running bulk GE-EMC simulation and performing MBAR analysis; `111` or `311` contains Python scripts for generating initial population, running surface GC-EMC simulation, performing MBAR analysis and plotting surface phase diagrams.高熵合金（High-Entropy Alloy, HEA）与高熵氧化物（High-Entropy Oxide, HEO）数据集分别存储于`Co0.25Mo0.45Fe0.1Ni0.1Cu0.1`和`Co0.2Ni0.2Mg0.2Zn0.2Mn0.2Al2O4`文件夹中。每个文件夹下均包含3个子文件夹：`models`子文件夹内存储用于训练MACE机器学习原子间势（Machine Learning Interatomic Potential, MLIP）的密度泛函理论（Density Functional Theory, DFT）数据集（格式为`.xyz`），以及训练完成的MACE MLIP模型文件（格式为`.model`）；`bulk`子文件夹内含用于生成初始种群、运行体相巨正则系综进化蒙特卡洛（Grand Canonical Ensemble Evolutionary Monte Carlo, GE-EMC）模拟并执行多态贝内特接受比分析法（Multistate Bennett Acceptance Ratio, MBAR）的Python脚本；`111`或`311`子文件夹内含用于生成初始种群、运行表面巨正则系综进化蒙特卡洛（Grand Canonical Ensemble Evolutionary Monte Carlo, GC-EMC）模拟、执行MBAR分析以及绘制表面相图的Python脚本。