mstar60

mstar60数据集由麦克马斯特大学材料科学与工程系创建，包含60个有效质量数据，来源于18种半导体材料。该数据集用于评估不同交换相关势在半导体有效质量计算中的性能。数据集的创建过程涉及从现有文献和Landolt-Börnstein数据库收集实验数据，并对多重实验值进行平均处理。mstar60数据集主要用于解决半导体材料设计和选择中的有效质量预测问题，特别是在高吞吐量计算材料研究中作为载流子迁移性、导电性和热电优值的指标。The mstar60 dataset, developed by the Department of Materials Science and Engineering at McMaster University, encompasses 60 effective mass data points sourced from 18 distinct semiconductor materials. This dataset is utilized to assess the performance of various exchange-correlation functionals during semiconductor effective mass calculations. The curation process of the dataset included collecting experimental data from peer-reviewed literature and the Landolt-Börnstein database, followed by averaging of multiple experimental measurements. The mstar60 dataset is primarily employed to address the effective mass prediction challenge in semiconductor material design and selection, acting as a key indicator for carrier mobility, electrical conductivity, and thermoelectric figure of merit in high-throughput computational materials science research.