Theoretical and machine learning models for reaction-barrier predictions: acrylate and methacrylate radical reactions

We propose density functional theory (DFT)- and random forest (RF)-based theoretical and machine learning (ML) models, respectively, for predicting reaction barriers (ΔE_TS) using acrylate and methacrylate radical reactions as representatives. DFT is used to determine 100 transition state (TS) structures of both radicals, after which the obtained data are used to determine theoretical relationships (explained with Bell-Evans-Polanyi or Brønsted-Evans-Polanyi (BEP) and Marcus-like models) between ΔE_TS and stabilization energy of the product. Next, we construct several theoretical regression models for predicting ΔE_TS of the representative reactions based on our theoretical analyses, presenting an RF-based ML model that eases ΔE_TS predictions by circumventing time-consuming DFT calculations. These theoretical and RF-based ML approaches will accelerate the advancement of material development.