Candidates Dataset

该数据集详细列出了用于自动化材料探索的候选实验条件，这些条件以实值向量形式表达，涵盖组成、结构和合成过程。该数据集旨在通过机器学习模型筛选出有前景的实验条件，并在实验进行时，根据目标函数值进行更新。规模上，该数据集包含N个候选条件，其中N代表实验条件的数量。该数据集的任务是实现材料探索以及实验条件的自动化探索与优化。

This dataset comprehensively lists candidate experimental conditions for automated materials exploration, which are expressed as real-valued vectors covering composition, structure and synthesis processes. It is designed to screen promising experimental conditions using machine learning models, and will be updated based on objective function values as experiments progress. The dataset contains N candidate conditions in total, where N represents the number of experimental conditions. The core task of this dataset is to enable automated exploration and optimization of both materials and experimental conditions.