  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 works 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 directly be
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. The backbone of our model, Orbital Mixer, uses MLP Mixer layers
within a simple, intuitive, and scalable architecture and achieves competitive
Hamiltonian and molecular orbital energy and coefficient prediction accuracies
compared to the state-of-the-art.
