With this goal in mind, we looked to optimize the synthesis of styrene-myrcene-DBI terpolymers with maximal 𝑇 𝑔 and minimal styrene incorporation utilizing machine learning (ML) methods with the experimental data collected from one-pot radical terpolymerization reactions. To achieve this, we varied not only the molar ratio of the three monomers in the reaction feed, but also explored different AIBN (Azobisisobutyronitrile) radical initiator molar ratios, reaction temperatures, and reaction durations, as shown in the reaction scheme in Figure . These experimental parameters can be adjusted to explore different combinations and their effects on the terpolymer properties. Although six input experimental parameters are listed, only five input parameters are independent due to the linear dependence between the feed molar ratios of styrene, myrcene, and DBI. As previously mentioned, we focus on maximizing the 𝑇 𝑔 and minimizing styrene incorporation of the final terpolymer. These terpolymer attributes are then measured offline by differential scanning calorimetry (DSC) and 1 H NMR, respectively.