Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis

TL;DR

This paper introduces a convolutional neural network-based dynamic classifier that monitors geometry optimization in real-time, significantly reducing computational waste and improving transferability across different catalytic intermediates in machine learning-driven catalyst design.

Contribution

The study presents a novel dynamic classifier approach that effectively predicts calculation outcomes during catalyst modeling, enhancing transferability and resource efficiency in computational catalysis.

Findings

Performs well across all intermediates in methane-to-methanol cycle

Generalizes to new intermediates and metal centers without accuracy loss

Reduces computational waste by over 50%

Abstract

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.