We then performed LASSO regression to reveal important variables. After the hyperparameter optimization (Figure ), LASSO regression afforded mean absolute error (MAE) of 5.6 GPa (Figure ). This metric is better than the reference metric of the mean model (MAE = 6.9 GPa), which assumes that there is no relationship between the input structure and output. The LASSO model retained 7 variables with positive and negative coefficients (Figure ). Four variables were extracted as substructures that are likely to increase Young's modulus, while three variables were extracted as substructures that are likely to decrease Young's modulus. As mentioned earlier, substructures with positive coefficients tend to form hydrogen bonds in crystals, which is consistent with the increase in Young's modulus. Substructures with negative coefficients should be related with the flexibility of the crystal. Crystals containing benzene rings often form layered structures with weak interlayer interactions. Many elastically bendable crystals have this arrangement, which is consistent with a lower Young's modulus . Crystals forming halogen-halogen interactions are known for their plastic deformation , which is included in the training dataset. It is thought that the halogens substructure remained in the variable selection of LASSO regression because crystals that undergo plastic deformation have a small Young's modulus. The counterintuitive finding is regarding aromatic OH. In the dataset, methyl gallate has the highest number of aromatic OH groups, containing three OH groups that form hydrogen bonds in the crystal. However, its Young's modulus is reported as 9.38 GPa , which doesn't even reach the average value. The existence of such data likely explains why aromatic OH showed a negative coefficient.