Low-Data Deep Quantum Chemical Learning for Accurate MP2 and Coupled-Cluster Correlations.

Accurate ab initio prediction of electronic energies is very expensive for macromolecules by explicitly solving post-Hartree-Fock equations. We here exploit the physically justified local correlation feature in a compact basis of small molecules and construct an expressive low-data deep neural network (dNN) model to obtain machine-learned electron correlation energies on par with MP2 and CCSD levels of theory for more complex molecules and different datasets that are not represented in the training set. We show that our dNN-powered model is data efficient and makes highly transferable predictions across alkanes of various lengths, organic molecules with non-covalent and biomolecular interactions, as well as water clusters of different sizes and morphologies.

Phosphinous Acid-Phosphinito Tetra-Icosahedral Au Nanoclusters for Electrocatalytic Oxygen Reduction.

While the formation of superatomic nanoclusters by the three-dimensional assembly of icosahedral units was predicted in 1987, the synthesis and structural determination of such clusters have proven to be incredibly challenging. Herein, we employ a mixed-ligand strategy to prepare phosphinous acid-phosphinito gold nanocluster Au(HOPPh)(OPPh)(TBBT) with a tetra-icosahedral kernel. Unlike expected, each icosahedral Au unit shares one vertex gold atom with two adjacent units, resulting in a "puckered" ring shape with a nuclearity of 48 in the kernel.