Stacked Ensemble Machine Learning for Range-Separation Parameters

Density
functional theory-based high-throughput materials and drug
discovery has achieved tremendous success in recent decades, but its
power on organic semiconducting molecules suffered catastrophically
from the self-interaction error until the nonempirical but expensive
optimally tuned range-separated hybrid (OT-RSH) functionals were developed.
An OT-RSH transitions from a short-range (semi)­local functional to
a long-range Hartree–Fock exchange at a distance characterized
by a molecule-specific range-separation parameter (ω). Herein,
we propose a stacked ensemble machine learning model that provides
an accelerated alternative of OT-RSH based on system-dependent structural
and electronic configurations. We trained ML-ωPBE, the first
functional in our series, using a database of 1970 molecules with
sufficient structural and functional diversity, and assessed its accuracy
and efficiency using another 1956 molecules. Compared with nonempirical
OT-ωPBE, ML-ωPBE reaches a mean absolute error of 0.00504a0–1 for optimal ω’s,
reduces the computational cost by 2.66 orders of magnitude, and achieves
comparable predictive power in optical properties.