Physics-informed Machine Learning Prediction of Hubbard Interaction Parameters

Accurate determination of Hubbard interaction parameters is essential for beyond-DFT approaches such as DFT+$U$, DFT+DMFT, and DFT+$U$+$V$ in correlated materials. In practice, however, these parameters are often chosen empirically, limiting their transferability across materials. Advanced computational approaches such as the constrained random-phase approximation (cRPA) provide a rigorous route for evaluating Hubbard interactions, but their computational cost remains a bottleneck for large-scale materials screening. Here, we present machine-learning (ML) models for predicting cRPA-derived Hubbard interaction parameters: effective on-site $U_{\rm eff}$, inter-site $V$, and Hund's coupling $J$ for transition-metal oxides (TMOs). We combine ensemble-learning models with a regression-based brute-force search (BFS) approach to achieve both predictive accuracy and explicit analytical expressions. We construct features that capture electronic, structural, and atomic properties, including the TM-$d$ bandwidth and TM-$d$/O-$p$ band-center separation, as physically motivated descriptors of localization and screening. Our ensemble models achieve RMSEs of 0.148 eV, 0.062 eV, and 0.007 eV for $U_{\rm eff}$, $V$, and $J$, respectively. The derived analytical forms directly relate $U_{\rm eff}$ to electron localization and TM-$d$/O-$p$ hybridization, suggest the importance of hybridization and structural compactness in determining $V$, and indicate that $J$ is governed primarily by elemental descriptors of the TM ion. Together, the present study provides an efficient approach for predicting cRPA-derived $U_{\rm eff}$, $V$, and $J$, while offering physical insight into the factors underlying these Hubbard interactions.