Computational chemistry simulations and experimental measurements are two conventional methods that are widely adopted in the field of materials science. However, using these two methods to accelerate materials discovery and design is difficult because they are time-consuming and inefficient. In the most cases density-functional theory (DFT) has been used for electronic structure calculations. Although there has been wide application of machine learning (ML) to many problems over the past years predicting the properties of molecules and materials using machine learning, with increasing role deep learning approaches. Advances in atomistic machine learning are making a large impact in many fields, including materials science , catalysis , and drug design . ML algorithms a learnable estimator between the input representations of materials and molecules and the output data being the physical or chemical properties of interest. These models have been used to predict a wide range of properties for different classes of materials, including point defects for neuromorphic computing , solar photocatalysts , and, especially both heterogeneous , and homogeneous , catalysts. Usually, first step of all ML-based approaches to predict material properties is the material representation. Crystal structures can be encoded in different ways, including features (often referred to as descriptors), 3D graphs with spacial information and corresponding node and edge attributes. In case of descriptors the main requirements are: low computational cost, distinguishability between similar structures and ability to encode both spatial and information, electronic structure and composition. Graph-based neural network architectures found to be effective across large molecular and materials datasets due to substatial number of learnable parameters, while descriptor-based approaches are One of the main applications of such models is a discovery and optimization of both heterogeneous and homogeneous catalysts . Novel catalysts can solve many environmental and energy challenges including solar fuels synthesis, moving to more atom-economic processes and valorization of atmospheric CO2 to small molecules and monomers. It still remains an open challenge to build models that can generalize across both elemental compositions of surfaces and configurations of adsorbates, and geometry crystalline surfaces. One of the most challenging tasks in catalyst discovery during the last ten years is the search for active and selective electrocatalysts for electrochemical CO2 reduction (CO2R) . One of current emission reduction strategies such as renewable energy sources, CO2 capture and storage (CCS) is the utilization of CO2 from industrial waste gases. The main pathways to utilize CO2 include reuse of it without conversion (e.g. enhanced oil recovery, supercritical CO2 or to convert the CO2 to a valuable small molecules and base chemicals for continuous production of polymers and fuels. Compared to photochemical and thermal processes electrochemical CO2 reduction (CO2R) found to be one of several promising strategies to valorize CO2 and prevent emissions. Electrochemical reactions usually operate at mild temperatures and pressure. They can be tuned to selective prod-ucts by variation in voltage, solvent, allow modular design, and provide opportunities to integrate renewable electricity with CO2 reduction in carbon-intensive industries and converters of peak-generated excess electric energy to methane and hydrogen for re-use at peak demand. In recent years, significant advances have been achieved in the development electrocatalysts for CO2R with continuous improvements in terms of selectivity (Faraday efficiency), energy efficiency, stability of catalysts during electrochemical process. However, to implement fully the potential benefits of new electrocatalysts in low cost, large scale CO2R electrolyzers requires advances in design and engineering novel catalytic materials . Applications of the density functional theory (DFT) allow to carrying out of a detailed description of mechanisms of catalytic processes. Typically, the computational study includes the relaxation of all intermediates, leading to the most energetically favourable conformers. Evaluation of the activation energies of all steps is also important. Due to increased computational complexity, DFT calculations are limited in memory and time for high-throughput catalyst screening . By training a surrogate model atomistic ML bypasses the computationally costly step of iterative solving the approximate Schrödinger equation. Trained structure-property model further is ready for an implementation on other examples, limited only by model's generalizability. A commonly used approach is using DFT to calculate corresponding energies of all steps of catalytic transformation on surface. The development of fast and accurate models for predicting of catalytic activity and selectivity is an important task during screening for novel catalysts. Deep learning aided modelling of heterogeneous catalytic processes handles all the known difficulties of modelling both organic and inorganic chemical structures. Learning on organic chemical space involves huge variety of structurally similar molecules and need to find low-energy conformers. Inorganic chemical spaces represents a large diversity in elements, lattice structures, coordination environments, and long-range interactions. The combination of these two chemical spaces in the form of heterogeneous catalytic processes leads to the complex problem of their exploitation. The first examples of computational catalyst design comes from the group of Jens Nørskov, who has pioneered the descriptor-based design approach and has applied it to the CO2 reduction process . Thus, the development of deep-learning methods, especially graphs neural networks with advanced featurization of inputs structures, for molecules and materials allows us to train models on broader datasets, with the possibility to use for millions of structures and solve problems noted above.