The fundamental promise of density functional theory is that the total energy of an electronic ground state can be completely characterized by its one particle reduced density . Despite this assurance, the analytical form of KED and inter-electronic repulsion potential as functionals of electron density is unknown. To bypass this problem, Kohn and Sham proposed a scheme where the density is partitioned into orbitals and computed from the approximated inter-electronic repulsion and kinetic energy density. The problem of kinetic energy, though hidden in KS scheme, remains an open challenge to this day . While in principle exact, Kohn-Sham equations cannot be applied to large systems due to prohibitive computational cost . In OFDFT, the total electron density can be computed from an Euler equation or imaginary time-evolution of a time-dependent hydrodynamical equation . As a result, the computational cost does not depend upon the number of electrons in the system explicitly, making the search for an accurate OFDFT method important. Recent efforts to employ OFDFT methods with the machine learning algorithms and pseudopotentials method manifest the usefulness of an accurate and general form of KED for simulations of large scale materials.