Machine-learned exchange-correlation functionals for molecules, solids, and reactive surfaces
CIDER26SS is a machine-learned exchange-correlation functional designed to accurately describe molecules, solids, and surfaces. It uses a Gaussian process framework trained on molecular and transition metal data to provide balanced accuracy for both systems. This method successfully resolves the CO/Pt puzzle and predicts properties for diverse materials outside its training set.