Subbulakshmi Ganesan

Abstract Accurate prediction of detonation performance is a critical challenge in the design and screening of energetic materials due to the complex and nonlinear interactions among their physicochemical and molecular descriptors. In this study, a comprehensive data-driven framework based on machine learning (ML) algorithms is developed to predict detonation pressure using a curated dataset of 222 distinct energetic compounds collected from the literature.

Abstract The accurate prediction of solubility and solvent properties in supercritical CO₂ systems remains a critical challenge in pharmaceutical process design due to the nonlinear and coupled effects of temperature and pressure. This study proposes a novel artificial intelligence-based modeling framework for predicting solvent density and paracetamol mole fraction under supercritical conditions using temperature and pressure as input variables.

G.Al-Assi, S. T.Abdalwahab, S.Ganesan, et al., “Surface-Engineered Nitrogen-Doped MXene Quantum Dots for Fluorescent Environmental Metal Ion Detection,” Surface and Interface Analysis (2026): 1–24, https://doi.org/10.1002/sia.70073. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.