# Evidential Deep Regression

Universal DFT Hamiltonian models quickly map atomic structure to electronic properties, but their predictions carry no reliability information. EviDTB attaches evidential deep regression to the SLEM backbone in DeePTB, learns only the residual between the converged Hamiltonian and a reference Hamiltonian built from the initial density, and returns a per-atom uncertainty score in a single forward pass. On 5,478 held-out structures, removing the 15% most uncertain structures lowers the 99th percentile of the mean local squared prediction error from 16.63 to 3.72 eV²; on 636 generated structures evaluated without retraining, the score has a Spearman correlation of 0.743 with that error.

Universal DFT Hamiltonian models quickly map atomic structure to electronic properties, but their predictions carry no reliability information. EviDTB attaches evidential deep regression to the SLEM backbone in DeePTB, learns only the residual between the converged Hamiltonian and a reference Hamiltonian built from the initial density, and returns a per-atom uncertainty score in a single forward pass. On 5,478 held-out structures, removing the 15% most uncertain structures lowers the 99th percentile of the mean local squared prediction error from 16.63 to 3.72 eV²; on 636 generated structures evaluated without retraining, the score has a Spearman correlation of 0.743 with that error.