Integration of Genetic Algorithm with Machine Learning for Properties Prediction

Chawuthai, Rathachai; Murathathunyaluk, Siripan; Amornratthamrong, Nalin; Arunchaipong, Run; Anantpinijwatna, Amata

doi:10.3303/CET25117170

Integration of Genetic Algorithm with Machine Learning for Properties Prediction

Author(s)

Chawuthai, Rathachai

Murathathunyaluk, Siripan

Amornratthamrong, Nalin

Arunchaipong, Run

Anantpinijwatna, Amata

Date Issued

January 1, 2025

Type

Article

DOI

10.3303/CET25117170

Abstract

Numerous studies have demonstrated that machine learning (ML) provides more accurate estimations of properties for oxygenated organic derivatives compared to the conventional Quantitative Structure-Property Relationship (QSPR) method. Consequently, ML’s predictive capabilities have been extended to encompass a broader range of properties, including Partition Coefficient, Boiling Point, and Solubility, among others, for oxygenated hydrocarbon derivatives. Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. The models are trained and validated using experimental data from published literature. Despite the accuracy of these property predictions, they have limited practical utility in industry, where specific property ranges are essential for processes. To address this, Genetic Algorithms (GA) are employed to design chemical compounds that meet industrial requirements. Integrating GA with ML could yield alternative chemical compounds, enhancing overall production processes by increasing economic potential, sustainability, and reducing environmental impact.

Citation

Chemical Engineering Transactions, 117, 1015-1020, 2025