Data for "Supervised machine learning methods for crystal structure prediction of the binary Cs-Te system"

Crystal structures, high-throughput calculations and trained machine learning models presented in the paper "Supervised machine learning methods for crystal structure prediction of the binary Cs-Te system".

crystal_datasets contains the input/output data sets of crystal structures for high-throughput calculations and ML models.

aiida_ht_calculations contains the data regarding the high-throughput DFT calculations.

ml_models contains the trained ML models.

Eeach zip-archive contains a jupyter-notebook examplifying how the data can be accessed and reused.

本数据集包含论文《二元Cs-Te体系晶体结构预测的监督机器学习方法》（Supervised machine learning methods for crystal structure prediction of the binary Cs-Te system）中呈现的晶体结构、高通量计算结果与训练完成的机器学习模型。

`crystal_datasets` 收录了面向高通量计算与机器学习模型的晶体结构输入输出数据集。

`aiida_ht_calculations` 存储了与高通量密度泛函理论（Density Functional Theory, DFT）计算相关的数据。

`ml_models` 包含训练完成的机器学习模型。

每个zip归档文件均配套一份Jupyter Notebook示例，用于演示该数据集的访问与复用方法。