  We present a machine-learned (ML) model of kinetic energy for orbital-free
density functional theory (OF-DFT) suitable for bulk light weight metals and
compounds made of group III-V elements. The functional is machine-learned with
Gaussian process regression (GPR) from data computed with Kohn-Sham DFT with
plane wave bases and local pseudopotentials. The dataset includes multiple
phases of unary, binary, and ternary compounds containing Li, Al, Mg, Si, As,
Ga, Sb, Na, Sn, P, and In. A total of 433 materials were used for training, and
18 strained structures were used for each material. Averaged (over the unit
cell) kinetic energy density is fitted as a function of averaged terms of the
4th order gradient expansion and the product of the density and effective
potential. The kinetic energy predicted by the model allows reproducing
energy-volume curves around equilibrium geometry with good accuracy. We show
that the GPR model beats linear and polynomial regressions. We also find that
unary compounds sample a wider region of the descriptor space than binary and
ternary compounds, and it is therefore important to include them in the
training set; a GPR model trained on a small number of unary compounds is able
to extrapolate relatively well to binary and ternary compounds but not vice
versa.
