Minimal Basis Iterative Stockholder: Atoms in Molecules for Force-Field Development

Atomic partial charges appear in the Coulomb term of many force-field models and can be derived from electronic structure calculations with a myriad of atoms-in-molecules (AIM) methods. More advanced models have also been proposed, using the distributed nature of the electron cloud and atomic multipoles. In this work, an electrostatic force field is defined through a concise approximation of the electron density, for which the Coulomb interaction is trivially evaluated. This approximate "pro-density" is expanded in a minimal basis of atom-centered s-type Slater density functions, whose parameters are optimized by minimizing the Kullback-Leibler divergence of the pro-density from a reference electron density, e.g. obtained from an electronic structure calculation. The proposed method, Minimal Basis Iterative Stockholder (MBIS), is a variant of the Hirshfeld AIM method but it can also be used as a density-fitting technique. An iterative algorithm to refine the pro-density is easily implemented with a linear-scaling computational cost, enabling applications to supramolecular systems. The benefits of the MBIS method are demonstrated with systematic applications to molecular databases and extended models of condensed phases. A comparison to 14 other AIM methods shows its effectiveness when modeling electrostatic interactions. MBIS is also suitable for rescaling atomic polarizabilities in the Tkatchenko-Sheffler scheme for dispersion interactions.Comment: 61 pages, 12 figures, 2 table

Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces

The excessive emissions of carbon dioxide (CO$_2$) into the atmosphere threaten to shift the CO$_2$ cycle planet-wide and induce unpredictable climate changes. Using artificial intelligence (AI) trained on high-throughput first principles based data for a broad family of oxides, we develop a strategy for a rational design of catalytic materials for converting CO$_2$ to fuels and other useful chemicals. We demonstrate that an electron transfer to the $\pi^*$-antibonding orbital of the adsorbed molecule and the associated bending of the initially linear molecule, previously proposed as the indicator of activation, are insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI model identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. This finding suggests using the C-O bond elongation as an indicator of CO$_2$ activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO$_2$ conversion, and a recipe to find more

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

Classical intermolecular potentials typically require an extensive parametrization procedure for any new compound considered. To do away with prior parametrization, we propose a combination of physics-based potentials with machine learning (ML), coined IPML, which is transferable across small neutral organic and biologically-relevant molecules. ML models provide on-the-fly predictions for environment-dependent local atomic properties: electrostatic multipole coefficients (significant error reduction compared to previously reported), the population and decay rate of valence atomic densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O atoms. These parameters enable accurate calculations of intermolecular contributions---electrostatics, charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials, this model is transferable in its ability to handle new molecules and conformations without explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight global parameters---optimized once and for all across compounds. We validate IPML on various gas-phase dimers at and away from equilibrium separation, where we obtain mean absolute errors between 0.4 and 0.7 kcal/mol for several chemically and conformationally diverse datasets representative of non-covalent interactions in biologically-relevant molecules. We further focus on hydrogen-bonded complexes---essential but challenging due to their directional nature---where datasets of DNA base pairs and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and as a first look, we consider IPML in denser systems: water clusters, supramolecular host-guest complexes, and the benzene crystal.Comment: 15 pages, 9 figure