Machine Learning-Enabled Development of Accurate Force Fields for Refrigerants

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.

Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models

While Bayesian inference is the gold standard for uncertainty quantification and propagation, its use within physical chemistry encounters formidable computational barriers. These bottlenecks are magnified for modeling data with many independent variables, such as X-ray/neutron scattering patterns and electromagnetic spectra. To address this challenge, we employ local Gaussian process (LGP) surrogate models to accelerate Bayesian optimization over these complex thermophysical properties. The time-complexity of the LGPs scales linearly in the number of independent variables, in stark contrast to the computationally expensive cubic scaling of conventional Gaussian processes. To illustrate the method, we trained a LGP surrogate model on the radial distribution function of liquid neon and observed a 1,760,000-fold speed-up compared to molecular dynamics simulation, beating a conventional GP by three orders-of-magnitude. We conclude that LGPs are robust and efficient surrogate models poised to expand the application of Bayesian inference in molecular simulations to a broad spectrum of experimental data.

Ionic Liquids for the Separation of Fluorocarbon Refrigerant Mixtures

This review discusses the research being performed on ionic liquids for the separation of fluorocarbon refrigerant mixtures. Fluorocarbon refrigerants, invented in 1928 by Thomas Midgley Jr., are a unique class of working fluids that are used in a variety of applications including refrigeration. Fluorocarbon refrigerants can be categorized into four generations: chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, and hydrofluoroolefins. Each generation of refrigerants solved a key problem from the previous generation; however, each new generation has relied on more complex mixtures that are often zeotropic, near azeotropic, or azeotropic. The complexity of the refrigerants used and the fact that many refrigerants form azeotropes when mixed makes handling the refrigerants at end of life extremely difficult. Today, less than 3% of refrigerants that enter the market are recycled. This is due to a lack of technology in the refrigerant reclaim market that would allow for these complex, azeotropic refrigerant mixtures to be separated into their components in order to be effectively reused, recycled, and if needed repurposed. As the market for recovering and reclaiming refrigerants continues to grow, there is a strong need for separation technology. Ionic liquids show promise for separating azeotropic refrigerant mixtures as an entrainer in extractive distillation process. Ionic liquids have been investigated with refrigerants for this application since the early 2000s. This review will provide a comprehensive summary of the physical property measurements, equations of state modeling, molecular simulations, separation techniques, and unique materials unitizing ionic liquids for the development of an ionic-liquid-based separation process for azeotropic refrigerant mixtures.