## 2 Data Description  
In this study, a dataset consisting of 176 data points was gathered from different studies reported in the literature, including experimental and MD simulation data points [3, 16, 18, 21, 25, 30, 36, 37, 38, 39, 40, 41]. While the experimental dataset was selected due to its high quality, public availability, and strong relevance to our study objectives, simulation data was also incorporated to address the limitations posed by the scarcity of experimental measurements. Simulation offers a controlled environment in which various variables can be systematically manipulated, enabling exploration across a wider parameter space. This approach allows us to generate meaningful findings that support our research objectives, particularly in areas where experimental data is difficult, costly, or time-consuming to obtain. Three input parameters including pressure (MPa), temperature (K), and salinity (mol/L) were considered in the data modeling process. The descriptive statistics for these parameters, presented in Table 2, highlight the diversity of the collected dataset. Notably, the mean values of pressure, temperature, salinity, and DC are 12.15 MPa, 320.06 K, 2.35 mol/L, and 2.07 \(\times 10^{-9}\) m\({}^{2}\)/s, respectively, with corresponding standard deviations reflecting significant variability, especially for salinity (2.42 mol/L) and pressure (7.99 MPa). The dataset covers values from 0.10 MPa to 30.00 MPa for Pressure, 286.15 K to 398.00 K for temperature, and 0 mol/L to 6.76 mol/L. Quartile analysis provides additional insights into  
Figure 2: Workflow of the machine learning framework for CO\({}_{2}\) DC prediction. the data distribution, with Q1, Q2 (median), and Q3 values indicating lower, central, and upper thresholds, as detailed in Table 2. These descriptive statistics of dataset collectively make it suitable for the use of ML modeling and analysis. To support the performance comparison of the developed models, a radar chart was created using R\({}^{2}\), RMSE, and MAE values for each model. These metrics were normalized to a 0-1 scale to enable fair and consistent visual comparison, the methodology for its construction involved use of Python 3.9 with the pandas and matplotlib libraries. In addition, model interpretability was enhanced using Shapley Additive Explanations (SHAP). SHAP values were calculated for the RF model using the SHAP library (version 0.45.0), allowing for the quantitative assessment of each input feature's impact on the predicted diffusion coefficient. The SHAP plots, which are also presented in Section 3, help to identify and visualize the influence of temperature, pressure, and salinity on model output. All ML models were implemented using Python 3.9. The RF and GBR models were developed using the scikit-learn library (version 1.2.2), while the XGBoost model was implemented using the XGBoost library (version 1.7.5). The Pearson correlation coefficients among pressure, temperature, salinity, and DC are visualized in Figure 3 as a heatmap. The correlation coefficients range between +1 and \(-1\), where values near +1 or \(-1\) indicate strong positive or negative linear relationships, respectively. The DC shows a moderate positive correlation with temperature (0.30), while salinity has a weak negative correlation (\(-0.21\)), and pressure (P) exhibits an almost negligible correlation (0.02) with DC. These weak correlations suggest that simple linear relationships may not fully explain the interactions between these input variables and output DC. Therefore, advanced ML models such as RF, GBR, and XGBoost are needed to capture complex, non-linear relationships and improve the prediction of the DC. Figure 4 displays violin plots that reveal the distribution of each variable, highlighting potential outliers. To make the dataset suitable for developing ML models, the parameter values were normalized due to their varying scales across.