Active-Learning Assisted General Framework for Efficient Parameterization of Force-Fields

This work presents an efficient approach to optimizing force field parameters for sulfone molecules using a combination of genetic algorithms (GA) and Gaussian process regression (GPR). Sulfone-based electrolytes are of significant interest in energy storage applications, where accurate modeling of their structural and transport properties is essential. Traditional force field parametrization methods are often computationally expensive and require extensive manual intervention. By integrating GA and GPR, our active learning framework addresses these challenges by achieving optimized parameters in 12 iterations using only 300 data points, significantly outperforming previous attempts requiring thousands of iterations and parameters. We demonstrate the efficiency of our method through a comparison with state-of-the-art techniques, including Bayesian Optimization. The optimized GA-GPR force field was validated against experimental and reference data, including density, viscosity, diffusion coefficients, and surface tension. The results demonstrated excellent agreement between GA-GPR predictions and experimental values, outperforming the widely used OPLS force field. The GA-GPR model accurately captured both bulk and interfacial properties, effectively describing molecular mobility, caging effects, and interfacial arrangements. Furthermore, the transferability of the GA-GPR force field across different temperatures and sulfone structures underscores its robustness and versatility. Our study provides a reliable and transferable force field for sulfone molecules, significantly enhancing the accuracy and efficiency of molecular simulations. This work establishes a strong foundation for future machine learning-driven force field development, applicable to complex molecular systems.