Overall, our work on e c (r) provides a ground for developing improved local MPAC functionals-either through traditional MPAC local interpolation schemes or via machine learning approaches that map HF density/orbital features to E c via e c (r) . Future work will aim to reduce the high computational cost of λ-FCI for larger systems by developing a wavefunction protocol to generate extensive datasets of e c (r). Then, we will leverage the power of local energy loss along with this data to create robust E c machine learning models.