chemistry, many approaches use atom-or geometry-specific feature representations and kernel-based or neural-network (NN) ML architectures. Recent studies focus on the featurization of atomic systems in abstracted representationssuch as quantum mechanical properties obtained from lowcost electronic structure calculations -and the utilization of novel graph neural network techniques to improve learning efficiency and generalizability. Several ML methods have been developed for the prediction of high-level (coupledcluster) correlation energies based on quantum mechanical features from a mean-field-level (i.e., HF theory or DFT) electronic structure calculation. For example, authors of OrbNet utilized a graph neural network architecture to predict high-quality electronic-structure energies based on features obtained from low-cost/minimal basis mean-field electronic structure methods. Different graph neural networks have recently achieved great successes in predicting the quantum mechanical properties of molecules. In previous works MPNNs with the appropriate message, update, and output functions demonstrated a useful inductive bias for predicting molecular properties, outperforming several strong baselines and eliminating the need for complicated feature engineering. An important task is to design MPNNs that can generalize effectively to larger graphs than those appearing in the training set or at least work with benchmarks designed to expose issues with generalization across graph sizes. Generalizing to larger molecule sizes seems particularly challenging when using spatial information. Interatomic interactions (covalent and non-covalent) are the determining factor in catalytic processes; in particular, they must be taken into account in modelling the adsorption energy. Therefore, we consider it important to introduce descriptors describing interatomic electrostatic interactions as attributes of edges. While orbitalorbital interactions together with information on the electronic state of atoms can be versatile node attributes. Also, several requirements are important in feature selection, such as isotropy of space, isometry of space, invariant concerning permutation of atomic indices, continuous and computationally cheap. Due to computational limitations, we don't use hybrid DFT/GNN approaches. As interatomic force matrices, we have chosen Coulomb Matrix as a simple descriptor for the electrostatic interaction between nuclei and Sine and Ewald sum matrices as potentials of electrostatic interactions in periodic (or pseudo-periodic) systems. As the representation of orbital-field matrix (OFM) based on the distribution of valence shell electrons of central and surrounding atoms.