The large number of parameters in the RPFs offers a mechanism for fine-tuning DFTB3's performance, enabling it to more accurately capture specific physical processes that might be deficient in its available parameterizations. However, caution is warranted when utilizing this flexibility. Over-fitting to a specific dataset can compromise transferability to other systems or conditions, potentially limiting the model's broader applicability. Additionally, any modifications to the RPFs should align with established physical principles to maintain the model's reliability. To ensure the efficacy and robustness of fitted RPFs, it is crucial to conduct thorough validation against independent data, thereby assessing both their accuracy and predictive power.