In Despite the observed improvement at the cost of resource-intensive reference data, energy prediction can be further enhanced by optimizing the architecture of the ML model. All tested models only consider short-range interactions while neglecting long-range interactions, like dispersion which has been validated to be important in TMCs. The pivotal role of dispersion in TMCs also explained why models trained on the tmQM-wB97MV data set outperform those on tmQM data set because the reference data of the former was calculated from ωB97M-V method which includes a better nonlocal correction than D3BJ. These findings also indicate that the tested models implicitly capture nonlocal interactions to a limited extent and the incorporation of embeddings related to long-range interactions will further improve the capability of ML models.