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Machine Learning-Guided Prediction of Hydroformylation.

Haonan Shi^1,2, Chaoren Shen^1,2, Zheng Huang^1,3

¹Chang-Kung Chuang Institute, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, China.

Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry

|October 29, 2024

Summary

Machine learning models accurately predict hydroformylation yield and linear selectivity for 1-octene using physical organic chemistry descriptors. This approach maps reaction conditions, validating predictions with experimental data.

Keywords:

Dataset Descriptor Hydroformylation Machine learning Selectivity prediction

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Area of Science:

Catalysis
Chemical Engineering
Computational Chemistry

Background:

Hydroformylation of 1-octene is crucial for producing valuable chemicals.
Predicting reaction outcomes like yield and selectivity is essential for process optimization.
Existing models may not fully capture the complex interplay of factors influencing hydroformylation.

Purpose of the Study:

To develop a holistic machine learning model for predicting yield and linear selectivity in 1-octene hydroformylation.
To utilize physical organic chemistry (POC) parameter-based descriptors for representing pre-catalyst molecular features.
To establish a comprehensive method for mapping correlations between reaction conditions and outcomes.

Main Methods:

Literature data compilation for 1-octene hydroformylation experiments.
Development of machine learning models using Random Forests (RF) and Extreme Gradient Boost (XGBoost) algorithms.
Feature engineering using physical organic chemistry (POC) parameters to describe pre-catalyst structures.

Main Results:

Machine learning models demonstrated high accuracy in predicting linear selectivity.
The developed models successfully mapped the correlations between various reaction conditions and the hydroformylation results.
Predicted outcomes showed strong agreement with experimental data, validating the model's performance.

Conclusions:

A robust machine learning framework can effectively predict hydroformylation yield and linear selectivity.
POC descriptors are valuable for representing molecular features in catalytic reactions.
The holistic model provides insights into optimizing hydroformylation processes through condition mapping.