Leveraging ab initio data at scale has enabled the development
of machine learning models capable of extremely accurate and fast
molecular property prediction. A central paradigm of many previous
studies focuses on generating predictions for only a fixed set of
properties. Recent lines of research instead aim to explicitly learn
the electronic structure via molecular wavefunctions, from which other
quantum chemical properties can be directly derived. While previous
methods generate predictions as a function of only the atomic configuration,
in this work we present an alternate approach that directly purposes
basis-dependent information to predict molecular electronic structure.
Our model, Orbital Mixer, is composed entirely of multi-layer perceptrons
(MLPs) using MLP-Mixer layers within a simple, intuitive, and scalable
architecture that achieves competitive Hamiltonian and molecular orbital
energy and coefficient prediction accuracies compared to the state-of-the-art.