Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis

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 a
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 optimizations on the fly, and
exploit its good performance and transferability in identifying geometry
optimization failures 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 the conversion
of 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 as arising from the use of electronic
structure and geometric information generated on-the-fly from density
functional theory calculations and the convolutional layer in the
dynamic classifier. When used in combination with 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.