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With the potential to sidestep the steep cost associated with high-level quantum-chemical calculations, machine learning (ML) has become an increasingly more viable approach in the field of theoretical and computational chemistry/physics over the past decade. In this work, we describe a novel molecular descriptor that goes beyond structural data and incorporates the wealth of information contained in semi-local descriptors of the electron density (ie, ρ (r) and▽ ρ (r))—the quantum-mechanical objects at the very heart of density functional theory (DFT). The proposed “semi-local density fingerprint”(SLDF) molecular descriptor transforms the most energetically-relevant information in ρ (r) into a unique and compact (system-size-independent) representation for each molecule. By construction, SLDFs are global molecular descriptors that are atomic-species independent, invariant to translations, rotations, and …