Expanding the scope of density derived electrostatic and chemical charge partitioning to thousands of atoms

The density derived electrostatic and chemical (DDEC/c3) method is implemented into the onetep program to compute net atomic charges (NACs), as well as higher-order atomic multipole moments, of molecules, dense solids, nanoclusters, liquids, and biomolecules using linear-scaling density functional theory (DFT) in a distributed memory parallel computing environment. For a >1000 atom model of the oxygenated myoglobin protein, the DDEC/c3 net charge of the adsorbed oxygen molecule is approximately -1e (in agreement with the Weiss model) using a dynamical mean field theory treatment of the iron atom, but much smaller in magnitude when using the generalized gradient approximation. For GaAs semiconducting nanorods, the system dipole moment using the DDEC/c3 NACs is about 5% higher in magnitude than the dipole computed directly from the quantum mechanical electron density distribution, and the DDEC/c3 NACs reproduce the electrostatic potential to within approximately 0.1 V on the nanorod’s solvent-accessible surface. As examples of conducting materials, we study (i) a 55-atom Pt cluster with an adsorbed CO molecule and (ii) the dense solids Mo2C and Pd3V. Our results for solid Mo2C and Pd3V confirm the necessity of a constraint enforcing exponentially decaying electron density in the tails of buried atoms

Introducing DDEC6 Atomic Population Analysis: Part 5. New Method to Compute Polarizabilities and Dispersion Coefficients

Polarizabilities and London dispersion forces are important to many chemical processes. Leading terms in these forces are often modeled using polarizabilities and Cn (n=6, 8, 9, 10 …) dispersion coefficients. Force fields for classical atomistic simulations can be constructed using atom-in-material dispersion coefficients and polarizabilities. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and m-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (e.g., polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C6 coefficients. Overall, MCLF has improved accuracy and lower computational cost than the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C8, C9, and C10 dispersion coefficients and Quantum Drude Oscillator parameters. For sufficiently large systems, our method’s required computational time and memory scale linearly with increasing system size. This is a huge improvement over the cubic computational time of direct matrix inversion. As demonstrations, we study an ice crystal containing >250,000 atoms in the unit cell and the HIV reverse transcriptase enzyme complexed with an inhibiter molecule. This method should find widespread applications to parameterize classical force fields and DFT+dispersion methods