King Khalid University

The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.

The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.

This study builds Ti3C2Tx MXene/n-type monocrystalline silicon heterojunction solar cells, exposes them to 50 MeV protons and 1 MeV electrons (electron fluence 10¹²–10¹⁵ particles/cm², proton 10¹²–10¹⁴ particles/cm²), characterizes them with XRD, Raman, SEM/TEM, EBSD, APT and LAMMPS atomistic simulation, and couples a physics-informed digital twin with Random Forest Regression, reporting over 84% retention of initial power conversion efficiency after irradiation versus about 55% for conventional Si cells, with R² > 0.96 for predictions of VOC, JSC, FF and PCE.

This study builds Ti3C2Tx MXene/n-type monocrystalline silicon heterojunction solar cells, exposes them to 50 MeV protons and 1 MeV electrons (electron fluence 10¹²–10¹⁵ particles/cm², proton 10¹²–10¹⁴ particles/cm²), characterizes them with XRD, Raman, SEM/TEM, EBSD, APT and LAMMPS atomistic simulation, and couples a physics-informed digital twin with Random Forest Regression, reporting over 84% retention of initial power conversion efficiency after irradiation versus about 55% for conventional Si cells, with R² > 0.96 for predictions of VOC, JSC, FF and PCE.