  The Kohn-Sham equations underlie many important applications such as the
discovery of new catalysts. Recent machine learning work on catalyst modeling
has focused on prediction of the energy, but has so far not yet demonstrated
significant out-of-distribution generalization. Here we investigate another
approach based on the pointwise learning of the Kohn-Sham charge-density. On a
new dataset of bulk catalysts with charge densities, we show density models can
generalize to new structures with combinations of elements not seen at train
time, a form of combinatorial generalization. We show that over 80% of binary
and ternary test cases achieve faster convergence than standard baselines in
Density Functional Theory, amounting to an average reduction of 13% in the
number of iterations required to reach convergence, which may be of independent
interest. Our results suggest that density learning is a viable alternative,
trading greater inference costs for a step towards combinatorial
generalization, a key property for applications.
