Exp -Gravimetric C p (J.g 1 .k higher than the corresponding value for other similar mass MOFs, e.g., Ni-MOF-74. This is a puzzling result, as the only change is the metal, and if we increase the atomic mass we expect monotonic increase of the heat capacity due to a shift of frequencies to lower values. A feature analysis of the machine learning model shows that 60% of the heat capacity prediction can be explained by knowing the atomic mass of all atoms in the material (see Figure and method section for details). The remaining 40% is the local geometry and chemistry. In the case of Mn-MOF-74, the feature analysis shows that the geometry is the decisive factor (see SI). Interestingly, if we take the Ni-MOF-74 geometry and simply replace the metal, the machine learning model gives similar values for all but Mn-MOF-74. For Mn-MOF-74, the feature analysis of the model shows that the higher predicted value of the heat capacity is caused by longer metal-linker bonds. These weaker bonds shift the frequencies to lower values. That machine learning gives us this explanation illustrates the importance of aligning the features of the model with the underlying physics.