Machine learning models are widely used in computational chemistry to predict experimental data and electronic properties across various chemical systems, including batteries , supercapacitors , thermoelectric and photoelectric compounds, and are used in catalysis and drug design and to explore the properties of materials and their surfaces in extreme environments. The key point of machine learning (ML) methods is structure representation, or the encoding of the information of the atomic environment in a given compound or chemical system. The two most commonly used approaches to structure representation are graph neural networks (GNN) and high-dimensional neural networks (HDNN) . We briefly contrast them with each other below.