Grain boundary solute segregation influences most bulk material properties, and understanding solute thermodynamics at grain boundaries is critical for engineering them. However, the vast grain boundary space in polycrystals is challenging to evaluate due to its size, especially for the intrinsically hard-to-compute segregation excess entropy. Here data science methods are used to generate a database of site-wise grain boundary segregation entropy spectra for 155 dilute binary alloys within the harmonic approximation. The spectral framework allows scale bridging between the calculated atomistic site-wise energy-entropy spectra and macroscopic segregation entropy estimates. The results affirm that macroscopic averaging is not sufficient: a spectral treatment of grain boundary segregation is needed to accurately model bulk temperature dependence of grain boundary solute segregation. The calculated spectral entropy database and thermodynamic framework can be applied for both understanding segregation experiments and alloy design exercises, paving the way to a finite-temperature grain boundary genome.

晶界溶质偏析影响大多数散装材料的性能，了解晶界处的溶质热力学对于对其进行工程设计至关重要。然而，多晶中巨大的晶界空间由于其尺寸而难以评估，特别是对于本质上难以计算的偏析过剩熵。这里使用数据科学方法在谐波近似内生成 155 种稀二元合金的位点晶界偏析熵谱数据库。谱框架允许在计算的原子位点能量熵谱和宏观偏析熵估计之间进行尺度桥接。结果证实，宏观平均是不够的：需要对晶界偏析进行光谱处理，以准确模拟晶界溶质偏析的整体温度依赖性。计算出的谱熵数据库和热力学框架可用于理解偏析实验和合金设计练习，为有限温度晶界基因组铺平道路。