colabfit/discrepencies_and_error_metrics_NPJ_2023_vacancy_enhanced_training_set

差异和误差指标 NPJ 2023 增强空位训练集，包括一些含有空位的结构。该数据集是在原始mlearn_Si_train数据集的基础上修改而成，目的是开发具有更好扩散性分数的模型，通过替换约54%的数据为含有迁移间隙原子的结构。增强验证集包含总共50个结构，包括从原始训练数据集中替换的120个结构中随机选取的20个结构，来自AIMD模拟的11个空位罕见事件（RE）快照，以及19个间隙RE快照。还构建了间隙RE和空位RE测试集，每个集包含100个原子配置快照，分别来自1230 K下AIMD模拟的单个迁移空位或间隙。数据集列中存储的附加细节以"dataset_"为前缀。

Structures from discrepencies_and_error_metrics_NPJ_2023 training set; includes some structures with vacancies. The dataset is modified from the original mlearn_Si_train dataset, with the purpose of developing models with better diffusivity scores by replacing ~54% of the data with structures containing migrating interstitials. The enhanced validation set contains 50 total structures, consisting of 20 structures randomly selected from the 120 replaced structures of the original training dataset, 11 snapshots with vacancy rare events (RE) from AIMD simulations, and 19 snapshots with interstitial RE from AIMD simulations. We also construct interstitial-RE and vacancy-RE testing sets, each consisting of 100 snapshots of atomic configurations with a single migrating vacancy or interstitial, respectively, from AIMD simulations at 1230 K. Additional details stored in dataset columns prepended with "dataset_".