Machine Learning Model for the Prediction of Hubbard <i>U</i> Parameters and Its Application to Fe-O Systems.

ABSTRACT: Without incurring additional computational cost, the Hubbard model can prevalently address the electron self-interaction problems of the local or semilocal exchange-correlation functions within density functional theory. However, determining the value of the Hubbard parameter, U, promptly, efficiently, and accurately has been a long-standing challenge. Here, we develop a method for predicting the Hubbard U of iron oxides by establishing a potential relationship through machine learning fitting of structural fingerprints and the U evaluated by the linear response-constrained density functional theory method. This method performs well in calculating the properties of wüstite, hematite, and magnetite, aligning with experimental measurements or more costly hybrid functiona

SUBMITTER: Xia W 

PROVIDER: S-EPMC11603611 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature