Several approaches are being explored to address these limitations:
Machine Learning: Machine learning techniques are being used to develop more accurate and transferable force fields. These data-driven models can learn from large datasets and potentially offer better performance than traditional methods. Hybrid Models: Combining quantum mechanical calculations with classical force fields can improve accuracy while managing computational costs. These hybrid models can offer a balance between accuracy and efficiency. Improved Parameterization Techniques: Advances in parameterization methods, including the use of high-throughput screening and automated optimization, can accelerate the development of new force fields.
