  DFT+U is a widely used treatment in the density functional theory (DFT) to
deal with correlated materials that contain open-shell elements, whereby the
quantitative and sometimes even qualitative failures of local and semilocal
approximations can be corrected without much computational overhead. However,
finding appropriate U parameters for a given system is non-trivial and usually
requires computationally intensive and cumbersome first-principles
calculations. In this Letter, we address this issue by building a machine
learning (ML) model to predict material-specific U parameters only from the
structural information. An ML model is trained for the Mn-O chemical system by
calibrating their DFT+U electronic structures with the hybrid functional
results of more than Mn-O 3000 structures. The model allows us to determine a
reliable U value (MAE=0.128 eV, R2=0.97) for any given structure at nearly no
computational cost; yet the obtained U value is as good as that obtained from
the conventional first-principles methods. Further analysis reveals that the U
value is primarily determined by the local chemical structure, especially the
bond lengths, and this property is well captured by the ML model developed in
this work. This concept of the ML U model is universally applicable and can
considerably ease the usage of the DFT+U method by providing
structure-specific, readily accessible U values.
