Accurate predictive model of band gap with selected important features based on explainable machine learning

ArXi:2503.04492v3 Announce Type: replace-cross In the rapidly advancing field of materials informatics, nonlinear machine learning models have nstrated exceptional predictive capabilities for material properties. However, their black-box nature limits interpretability, and they may incorporate features that do not contribute to -- or even deteriorate -- model performance.