Deciphering melting behaviors of energetic compounds using interpretable Machine learning for melt-castable applications

Melting behaviors of energetic compounds determine their thermo-responsive properties, which affects the manufacturing operations and structural stabilities. However, the decisive physicochemical mechanisms for melting behaviors of energetic compounds are absent, which hurdles the acceptancy and generalization of prediction models for rational molecular design. Herein, an interpretable machine learning (IML) approach was proposed to construct accurate prediction models for melting point values and melt-castable potentials of energetic compounds. A high-quality melting-point dataset comprising 239 neutral CHNO compounds was obtained, and the data was collected with strict criteria involving chemical formulations, crystal structures and experimental measurement operations. Tailored descriptors, which involve the priori insights for melting behaviors, were adopted for IML algorithms. The extreme gradient boosting model presents the best prediction accuracy (85 %) for the classification of melting point values between 273 K and 650 K; while 87 % accuracy is achieved for the classification of melt-castable property. Importantly, model interpretation assists to reveal that the close contacts between OC and NC, dipole, oxygen balance and the cohesive energy are decisive features for melting point prediction; while enthalpy of sublimation and polar surface area are paramount for the identification of melt-castable property. The ability to determine melting behaviors using IML will broaden the high-throughput screening of energetic compounds and offer valuable guidance towards molecular design.