  Orbital-free density functional theory (OFDFT) is a quantum chemistry
formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT,
which is increasingly desired for contemporary molecular research. However, its
accuracy is limited by the kinetic energy density functional, which is
notoriously hard to approximate for non-periodic molecular systems. Here we
propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a
deep learning functional model. We build the essential non-locality into the
model, which is made affordable by the concise density representation as
expansion coefficients under an atomic basis. With techniques to address
unconventional learning challenges therein, M-OFDFT achieves a comparable
accuracy with Kohn-Sham DFT on a wide range of molecules untouched by OFDFT
before. More attractively, M-OFDFT extrapolates well to molecules much larger
than those seen in training, which unleashes the appealing scaling of OFDFT for
studying large molecules including proteins, representing an advancement of the
accuracy-efficiency trade-off frontier in quantum chemistry.
