  Virtual high throughput screening (VHTS) and machine learning (ML) have
greatly accelerated the design of single-site transition-metal catalysts. VHTS
of catalysts, however, is often accompanied with high calculation failure rate
and wasted computational resources due to the difficulty of simultaneously
converging all mechanistically relevant reactive intermediates to expected
geometries and electronic states. We demonstrate a dynamic classifier approach,
i.e., a convolutional neural network that monitors geometry optimization on the
fly, and exploit its good performance and transferability for catalyst design.
We show that the dynamic classifier performs well on all reactive intermediates
in the representative catalytic cycle of the radical rebound mechanism for
methane-to-methanol despite being trained on only one reactive intermediate.
The dynamic classifier also generalizes to chemically distinct intermediates
and metal centers absent from the training data without loss of accuracy or
model confidence. We rationalize this superior model transferability to the use
of on-the-fly electronic structure and geometric information generated from
density functional theory calculations and the convolutional layer in the
dynamic classifier. Combined with model uncertainty quantification, the dynamic
classifier saves more than half of the computational resources that would have
been wasted on unsuccessful calculations for all reactive intermediates being
considered.
