## 3 Results and Discussion
### Validation of Decomposition Degree Predictive Models and Comparison of Contributing Factors  
Firstly, a dataset for machine learning was created using the weight-loss ratio as an objective variable and the moisture content of the compost environment, degradation period, initial crystallinity, and features of \({}^{13}\)C-CP/MAS spectra as explanatory variables. The decomposition degree predictive model was constructed using RF and XGBoost machine learning algorithms to process these data. The results of the prediction of data validation are shown in Figure S4. \(R^{2}\) was 0.38 for RF and 0.08 for XGBoost. RMSE was 0.76% for RF and 1.30% for XGBoost. Thus, RF had a higher prediction accuracy than XGBoost. However, we considered that the degree of decomposition cannot be predicted accurately, because the bivariate plots of observed and predicted weight-loss ratios did not follow a diagonal line and the variation was substantial. Therefore, we constructed a decomposition degree predictive model in which the objective variable was changed to Mw. The results of the prediction of validation data and a comparison of the factors of importance are shown in Figure 2.  
\(R^{2}\) and RMSE were 0.78 and 10.78%, respectively. From these results, we found that a more accurate decomposition degree predictive model could be constructed by using Mw rather than weight-loss ratio as the objective variable. Next, we discussed the contributing factors for prediction. The moisture content in the compost environment and decomposition period had a significant effect on the degree of decomposition. The reason why the moisture content in the compost environment greatly affected the degradation degree was considered to be due to the primary degradation of PLA occurring by hydrolysis, and it was confirmed that the amount of moisture in the surrounding environment was extremely important during the early stage of degradation. In addition, the PC1 and PC2 obtained by PCA of the NMR spectra provided information on the crystalline and amorphous structure, which slightly affected the degree of decomposition. We also examined the construction of a decomposition degree predictive model using \({}^{1}\)H wide-line spectra. The results of the prediction of validation data and comparison of factors of importance are shown in Figure S5. \(R^{2}\) and RMSE were 0.73 and 12.42%, respectively. Based on these results, the predictive model constructed using the \({}^{1}\)H wide-line NMR spectra yielded lower predictive accuracy than using \({}^{13}\)C-CP/MAS spectra, which was likely due to the good separability of the carbon chain signal, and the fact that the crystal structure information could be obtained more clearly with \({}^{13}\)C-CP/MAS. Based on the importance of the predictive model, the moisture content in the compost environment and decomposition period had a significant effect on the degree of decomposition, while the factor of the crystalline or amorphous structure had little effect on the degree of decomposition. Furthermore, these results were similar to those which were obtained using \({}^{13}\)C-CP/MAS. Finally, the optimum decomposition degree, various analytical values, and experimental conditions were explored from virtual experiments that combined the constructed decomposition degree predictive model with Bayesian optimization. The NMR spectra were predicted by calculating the inner product of the PCA loadings and PC1 and PC2 scores at the optimal decomposition degree proposed by Bayesian optimization, and then calculating the restoration matrix. The stack diagram of the obtained predicted spectra and actual measured spectra is shown in Figure 3. Based on the comparison of the spectra, the spectra were predicted to have a higher crystal ratio.