Machine-learning-assisted high-throughput computational screening of the <i>n</i>-hexane cracking initiator

Xiaodong Hong; Yudong Shen; Zuwei Liao; Yongrong Yang

doi:10.1016/j.cjche.2025.07.002

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

Full Length Article | Updated：2026-03-26

- Machine-learning-assisted high-throughput computational screening of the n-hexane cracking initiator
- Machine-learning-assisted high-throughput computational screening of the n-hexane cracking initiator
- 中国化学工程学报（英文版） 2025年84卷第8期页码：190-200
- Affiliations：
  
  1. State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University,Hangzhou,China,310027
  2. Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University,Hangzhou,China,311215
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2025.07.002
  中图分类号：
- 纸质出版：2025
- Accepted：
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Xiaodong Hong, Yudong Shen, Zuwei Liao, 等. Machine-learning-assisted high-throughput computational screening of the n-hexane cracking initiator[J]. 中国化学工程学报（英文版）, 2025,84(8):190-200. DOI: 10.1016/j.cjche.2025.07.002.

-hexane cracking initiator[J]. 中国化学工程学报, 2025, 84(8): 190-200.
Xiaodong Hong, Yudong Shen, Zuwei Liao, 等. Machine-learning-assisted high-throughput computational screening of the n-hexane cracking initiator[J]. 中国化学工程学报（英文版）, 2025,84(8):190-200. DOI: 10.1016/j.cjche.2025.07.002. DOI：

-hexane cracking initiator[J]. 中国化学工程学报, 2025, 84(8): 190-200. DOI： 10.1016/j.cjche.2025.07.002.

摘要

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.

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

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

DOI：10.1016/j.cjche.2025.07.002

摘要

Abstract

关键词

Keywords

references