## 1 Introduction
[PERSON] et al. (2013) [18], using microfluidics, showed that higher salinity inhibits diffusion due to stronger ion-water interactions. The Taylor dispersion method has been especially useful across a broad range of temperatures and pressures, as shown by [PERSON] et al. (2014) [16], who highlighted the positive effect of temperature on diffusivity due to enhanced molecular motion. [PERSON] and [PERSON] (2005) [19] employed the pendant drop technique to investigate gas-liquid interfacial dynamics, revealing key diffusion behaviors. [PERSON] et al. (1997) [20] used laser-induced fluorescence (LIF) to validate theoretical predictions under high-pressure conditions. Pressure decay methods have also shown significant sensitivity to ionic strength: [PERSON] et al. (2013) [21] and [PERSON] (2006) [22] found that increased salinity and ionic strength reduce CO\({}_{2}\) diffusivity by acting as physical barriers to molecular transport. Although experimental methods provide accurate CO\({}_{2}\) DC, they are time-consuming, costly, and require specialized instrumentation. As a result, empirical correlations have become a practical alternative, particularly when direct measurements are unavailable. For example, the Stokes-Einstein relation, as applied by [PERSON] et al. (2015) [23], uses viscosity, temperature, and solute radius but performs poorly in high-salinity systems (>5 M NaCl) due to ion pairing and viscosity effects. While it works for simple NaCl brines, it fails in multicomponent systems with divalent ions like Mg\({}^{2+}\) and Ca\({}^{2+}\). Similarly, the [PERSON] and [PERSON] (1955) correlation [24] incorporates temperature, viscosity, solute size, and an association parameter but is limited to dilute, non-electrolytic solutions. It neglects ionic strength and solute hydration, leading to significant errors in concentrated brines (>1 M salinity) [24; 25; 26]. Overall, these correlations struggle with complex reservoir conditions and cannot reliably predict DC in realistic, high-salinity brine systems. Beyond experiments and empirical correlations, molecular dynamics (MD) simulations offer atomic-scale insights into CO\({}_{2}\) diffusion in brine. Studies by [PERSON] et al. (2012) [25] and [PERSON] et al. (2022) [27] highlight the role of ion hydration and salinity in controlling diffusivity. While powerful, MD simulations require validation under extreme reservoir conditions and are often combined with experimental methods like Raman spectroscopy and pressure decay to improve reliability. These hybrid approaches help capture complex interactions involving multivalent ions and ionic strength effects. Traditional methods, including correlations and MD simulations, provide useful insights but face challenges with large datasets and non-linear interactions. Experimental techniques [28; 29; 30] are also time- and resource-intensive. Machine learning (ML) models address these issues by integrating experimental and simulation data to predict CO\({}_{2}\) diffusivity with high accuracy and efficiency [31]. [PERSON] et al. (2019) [32] and [PERSON] et al. (2020) [33] developed ML models--MKSVM-GA and PSO-ANFIS--for predicting CO\({}_{2}\) diffusivity in brines. However, both used limited datasets with narrow salinity and pressure ranges. While high accuracy was reported, the small sample sizes risk overfitting and limit generalization. Their model parameters and performance metrics are summarized in Table 1.  
The present study addresses limitations of previous works by developing a more robust and diverse predictive framework tailored for CCS applications. It aims to predict CO\({}_{2}\) diffusion coefficients in brine using advanced machine learning models--Random Forest (RF), Gradient Boosted Regression (GBR), and XGBoost--trained on a dataset of 176 experimental and simulation data points. The data span pressures of 0.1-30 MPa, temperatures of 286.15-398 K, and salinities up to 6 mol/L. RF was chosen for its robustness against overfitting, while GBR and XGBoost employ sequential learning to capture complex patterns.