But there are no exact constraints that would tell us how the LMF should look like in the bonding and generally in the valence region to achieve these goals. This is a major aspect this work addresses. The absence of first-principles guidelines to construct these parts of an LMF suggests that a data-driven approach may provide improved LMFs for valence properties, such as chemical energy differences. We therefore introduce so-called n-LMFs constructed as relatively shallow neural networks (NNs).