Abstract: A machine learning (ML) method for predicting an electronic structure of an atomic system. The method includes receiving an atomic identifier and an atomic position for atoms in the atomic system; receiving a basis set including rules for forming atomic orbitals of the atomic system; forming the atomic orbitals of the atomic system; and predicting an electronic structure of the atomic system based on the atom identifier, the atom position for the atoms in the atomic system, and the atomic orbitals of the atomic system. The ML method is capable of extremely accurate and fast molecular property prediction. The ML can directly purpose basis dependent information to predict molecular electronic structure. The ML method, which may be referred to as an orbital mixer model, uses multi-layer perception (MLP) mixer layers within a simple, intuitive, and scalable architecture to achieve competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies.