Machine-learning-assisted high-throughput computational screening of the n-hexane cracking initiator

Abstract

This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators. Artificial neural networks are applied to predict the chemical performance of initiators, using simulated pyrolysis data as the training dataset. Various feature extraction methods are utilized, and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures. High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene, propylene, and butadiene. The relative error between predicted and simulated values is less than 7%. Additionally, reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products. The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.