Innovating bio-lubricant design: utilizing Mordred descriptors for model development and thermal stability prediction of novel compounds

Purpose Developing bio-lubricants for high-temperature applications, such as engine operations, requires reliable thermal stability assessment. This study aims to create a predictive tool to evaluate thermal stability using existing organic compounds data. The model predicts the onset temperature (T onset) of bio-lubricant candidates derived from epoxidation with various amine nucleophiles, enhancing initial selection efficiency and reducing reliance on traditional trial-and-error methods. Design/methodology/approach Using the Python library Mordred, molecular descriptors were calculated from the SMILES structures of 126 compounds with known T onsets. These descriptors were inputs for machine learning models predicting the thermal stability of bio-lubricants. The models were evaluated on training and test data sets and validated with novel synthesized compounds. Findings The predictive model showed strong performance on the test data set, with an R ² of 0.93 and a root mean square error of 17.77. In external validation, the model estimated the thermal stability of a novel bio-lubricant compound (MA-EPO) at 290.7°C, closely matching the actual T onset of approximately 300°C. Originality/value This research introduces a method for predicting the thermal stability of bio-lubricants using machine learning and open-source libraries. This approach significantly advances the field by improving the efficiency of bio-lubricant development for high-temperature applications. It provides a cost-effective and time-saving alternative to traditional experimental methods, serving as a valuable resource for researchers and developers in the bio-lubricant industry.