2026/07/29 by Jiyeon Kim, Indukuru Ramesh Reddy, Bongjae Kim +1
Physics and Astronomy · #cond-mat.mtrl-sci #cond-mat.str-el
arxiv created 2026/07/29 · arxiv updated 2026/07/30
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.