Development of Prediction Models for the Self-Accelerating Decomposition Temperature of Organic Peroxides

Toshiharu Morishita, Hiromasa Kaneko

Research output: Contribution to journalArticlepeer-review

Abstract

Thermal risk assessment is very important in the primary stages of chemical compound development. In this study, a model to estimate the self-accelerated decomposition temperature of organic peroxides was developed. The structural information of compounds was used to calculate descriptors, on which partial least-squares (PLS) regression and support vector regression were applied for temperature prediction. Molecular mechanics and density functional theory calculations were performed before descriptor calculations, for structure optimization, using a genetic algorithm for variable selection. Structure optimization and variable selection immensely improved the prediction accuracy. Thus, a PLS model, with R2 = 0.95, root mean square error = 5.1 °C, and mean absolute error = 4.0 °C, exhibiting higher accuracy than existing self-accelerating decomposition temperature prediction models, was constructed.

Original languageEnglish
Pages (from-to)2429-2437
Number of pages9
JournalACS Omega
Volume7
Issue number2
DOIs
Publication statusPublished - 18 Jan 2022

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