Development of Accurate Transferable Hydrofluorocarbon Refrigerant Force Fields Using a Machine Learning and Optimization Approach

Key Points

• Transferable force fields can significantly enhance the accuracy of hydrofluorocarbon refrigerant simulations, making them more reliable.
• Integrating machine learning methods reduced development time by incorporating advanced data-driven techniques in the process.
• Observational analysis leveraging optimization strategies was central to developing these force fields, indicating substantial efficiency gains.
• These findings imply a potential shift towards faster and more adaptable frameworks in computational chemistry.