## 4 Conclusions  
Degradation experiments were conducted to obtain the indices for the external environment, such as moisture content in a compost environment. The data were obtained from the experiments used to develop a model for predicting the degree of degradation, which was constructed by machine learning. The predictive model accurately predicted the degree  
Figure 3: (**a**) 3D chemical structure of PLA from wide-angle neutron diffraction data [31], and the numbers on atoms were annotated with reference to [PERSON] et al. (2013) [25]. (**b**) The predicted \({}^{13}\)C-CP/MAS spectrum was constructed from the dot product calculation of the PCA loadings and predicted PCA scores obtained from the predictive model and Bayesian optimization. From left to right, the carbonyl group (C=O), methine group (CH), and methyl group (CH\({}_{3}\)) are shown. The experimental \({}^{13}\)C-CP/MAS spectra of S/A samples (**c**) exposed to the degradation environment for 3 weeks and (**d**) before the degradation experiment.  
of degradation by using the rate of decrease in Mw as the objective variable. The rank of importance of the influencing factors on the degree of degradation, from highest to lowest, was the moisture content in the compost environment, degradation period, information on crystalline and amorphous structure, and initial crystallinity. Furthermore, by combining the constructed predictive model with Bayesian optimization, it was possible to predict the experimental conditions and NMR spectra when the decomposition degree was optimized. Although the optimized decomposition degree did not exceed the measured value, the result may be improved by implementing other machine learning methods. In addition, by adding experimental conditions such as temperature and pH to this predictive model, it may be possible to construct a more accurate predictive model that can explore the decomposition factors in more detail. In addition, for virtual experiments on bio-based plastics other than PLA, the decomposition degree predictive model and Bayesian optimization used in this study might achieve optimal accuracy. As a result, it may be possible to reduce the experimental cost and obtain the experimental conditions and analytical values at an optimum decomposition degree while analyzing the decomposition factors. Knowledge obtained from such virtual experiments is expected to lead to the design and development of bio-based plastics.  
The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/6/2820/s1, Figure S1: Stacked plot of \({}^{13}\)C-CP/MAS and \({}^{1}\)H wide-line spectra of PLA during the compost-degradation experiments; Figure S2: PCA using the \({}^{13}\)C-CP/MAS spectra; Figure S3: PCA using the \({}^{1}\)H wide-line NMR spectra; Figure S4: The results of predictive modeling using the weight-loss ratio as the objective variable by RF and XGBoost; Figure S5: Predictive accuracy and importance of a predictive model of the degree of degradability using \({}^{1}\)H wide-line NMR spectra; Table S1: List of PLA samples molded by the press-molding machine.  
[PERSON], [PERSON] and J.K. designed the study; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] programmed the analytical tools; all authors helped to prepare the manuscript and approved its submission. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by a grant from the Agriculture, Forestry, and Fisheries Research Council, as well as the Strategic Innovation Program (SIP) from CAO (to J.K.).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for advice on the experiments.  
The authors declare no conflict of interest.