Compound‐Blocked Calibration Transfer in a Three‐API Finite‐Cluster GIAO‐DFT 13 C NMR Benchmark

ABSTRACT Calibration models can improve agreement between calculated and experimental 13 C NMR shifts without necessarily transferring to a new compound. We reanalyzed a literature‐derived finite‐cluster GIAO‐DFT benchmark comprising carbamazepine (CBZ), theophylline (THP), and ibuprofen (IBP) using compound‐blocked validation and added an independent protocol‐heterogeneous external challenge using lamivudine Form II. Uncalibrated predictions, a one‐parameter offset, two‐parameter affine scaling, and a three‐parameter quadratic model were compared. Across the 20 primary sites, raw RMSE was 1.311 ppm. Leave‐one‐compound‐out aggregate RMSE was 1.065 ppm for offset correction, 1.113 ppm for affine scaling, and 1.052 ppm for quadratic calibration; macro‐averaged RMSE was 0.915, 0.986, and 0.916 ppm, respectively. Offset correction was lower than affine scaling in all three internal folds. In the locked lamivudine challenge (eight 13 C sites), however, raw RMSE was 4.174 ppm, and all three transferred calibrations worsened error: 4.643 ppm offset, 4.623 ppm affine, and 4.592 ppm quadratic. Thus the external API does not support generalization of the internally favorable calibration behavior. Because the external calculation used a published Gaussian09 GIAO workflow rather than the historical finite‐cluster protocol, it is an out‐of‐domain robustness challenge rather than like‐for‐like validation. Fbias is interpreted only retrospectively. The results support a bounded conclusion: Calibration rankings in this small benchmark are fragile to API and protocol domain shift and require prospective validation under a harmonized computational workflow.