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This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combines multiple techniques to develop a feature set encompassing all relative aspects of polymer chemistry, to extract and explain correlations between features and Tg, and to develop and apply a high-throughput predictive model. In this work, we identify aspects of the chemistry that most impact Tg, including a parameter related to rotational degrees of freedom and a backbone index based on a steric hindrance parameter. Building on this scientific understanding, models are developed on different types of data to ensure robustness, and experimental validation is obtained through the testing of new polymer chemistry with remarkable Tg. The ability of our model to predict Tg shows that the relevant information is contained within the topological descriptors, while the requirement of non-linear manifold transformation of the data also shows that the relationships are complex and cannot be captured through traditional regression approaches. Building on the scientific understanding obtained from the correlation analyses, coupled with the model performance, it is shown that the rigidity and interaction dynamics of the polymer structure are key to tuning for achieving targeted performance. This work has implications for future rapid optimization of chemistries 

With an estimated global cost of $2.5 trillion per year, metal corrosion represents a major challenge across all industrial sectors. Numerous inorganic and organic corrosion inhibitors have been developed, but there are growing concerns about their toxicity and impact on the environment. Here, superior organic corrosion inhibitors based on indole-3-carboxaldehyde, a compound commonly found in the digestive system, and thiosemicarbazones, a safe class of ligands, were designed and studied for mild steel in pH 1 sulfuric acid solutions. Electroanalytical techniques and gravimetric tests revealed inhibition efficiencies as high as 98.9% at 30 °C. Models using Langmuir isotherms gave adsorption equilibrium constants Kads of 2 to 9 × 104 M–1 and corresponding Gibbs free energies of adsorption (ΔGads) as high as −41.44 kJ mol–1, indicating their chemisorption. SEM images confirmed the efficacy of these corrosion inhibitors, as surface features showed limited to no changes after tests. Surface analysis by XPS and LC-MS revealed inhibitor concentrations on the order of 0.7 to 1.8 μg cm–2 for the best compounds, further underlining their performance at low concentrations. Mapping of the surface by MALDI-MS further confirmed the homogeneous coating of the steel surface, with no visible fluctuations in concentrations. As all inhibitors shared the same indole thiosemicarbazone platform, unique structure–performance relationships were drawn from theoretical calculations. Notably, DFT and AIMD explained the differences in performance, highlighting the role of side groups in the distribution of the molecular orbitals and the role of water molecules in enhancing the electronic properties of the organic corrosion inhibitors and promoting their chemisorption.