  Accelerated discovery with machine learning (ML) has begun to provide the
advances in efficiency needed to overcome the combinatorial challenge of
computational materials design. Nevertheless, ML-accelerated discovery both
inherits the biases of training data derived from density functional theory
(DFT) and leads to many attempted calculations that are doomed to fail. Many
compelling functional materials and catalytic processes involve strained
chemical bonds, open-shell radicals and diradicals, or metal-organic bonds to
open-shell transition-metal centers. Although promising targets, these
materials present unique challenges for electronic structure methods and
combinatorial challenges for their discovery. In this Perspective, we describe
the advances needed in accuracy, efficiency, and approach beyond what is
typical in conventional DFT-based ML workflows. These challenges have begun to
be addressed through ML models trained to predict the results of multiple
methods or the differences between them, enabling quantitative sensitivity
analysis. For DFT to be trusted for a given data point in a high-throughput
screen, it must pass a series of tests. ML models that predict the likelihood
of calculation success and detect the presence of strong correlation will
enable rapid diagnoses and adaptation strategies. These "decision engines"
represent the first steps toward autonomous workflows that avoid the need for
expert determination of the robustness of DFT-based materials discoveries.
