We presented several graph neural networks (GNNs) in our work, including convolutional and message passing architectures with physically informed node and edge attributes for atomistic systems. We demonstrate an improvement in adsorption energy predictions on the OC20 dataset using our proposed architecture in terms of the mean absolute error of predicted energy and energy within threshold metrics. Proposed architectures are stable to overfitting and can be used for the prediction of experimental and quantum chemical properties of a wide spectrum of materials and molecules. We introduced two GNN architectures (EdgeUpdateNet and OFMNet) with edge fingerprints as elements of interatomic interaction matrices (Coulomb matrix, Ewald sum matrix, Sine matrix). For node fingerprints, we use elements of the orbital field matrix (OFM), a one-hot representation of the electronic state of atoms with surrounding atomic orbitals. The developed models using physically informed fingerprints show an improvement in the accuracy of adsorption energy predictions. Especially significant improvements are observed in out of domain examples for both adsorbates and catalysts. The generalizability and extrapolation capabilities of the proposed architectures also make GNNs feasible for use in catalyst screening in the vast chemical space. The code with scripts, models and configurations used is available at .