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The Molecular Structure of cis-FONO

The molecular structure of cis-FONO has been determined with the CCSD(T) correlation method using an spdf quality basis set. In agreement with previous coupled-cluster calculations but in disagreement with density functional theory, cis-FONO is found to exhibit normal bond distances. The quadratic and cubic force fields of cis-FONO have also been determined in order to evaluate the effect of vibrational averaging on the molecular geometry. Vibrational averaging is found to increase bond distances, as expected, but it does not affect the qualitative nature of the bonding. The CCSD(T)/spdf harmonic frequencies of cis-FONO support our previous assertion that a band observed at 1200 /cm is a combination band (upsilon(sub 3) + upsilon(sub 4)), and not a fundamental.

Lee, Timothy J.↗

Materials Data on ZnH12(NO3)2 by Materials Project

ZnNHO3(H2)5HNO3 crystallizes in the monoclinic P2_1/c space group. The structure is zero-dimensional and consists of sixteen hydrogen molecules, four molecular hydrogen molecules, four nitric acid molecules, and four ZnNHO3 clusters. In each ZnNHO3 cluster, Zn2+ is bonded in a distorted linear geometry to one H1+ and one O2- atom. The Zn–H bond length is 1.51 Å. The Zn–O bond length is 1.89 Å. N1- is bonded in a trigonal planar geometry to three O2- atoms. There are a spread of N–O bond distances ranging from 1.23–1.37 Å. H1+ is bonded in a single-bond geometry to one Zn2+ atom. There are three inequivalent O2- sites. In the first O2- site, O2- is bonded in a bent 120 degrees geometry to one Zn2+ and one N1- atom. In the second O2- site, O2- is bonded in a single-bond geometry to one N1- atom. In the third O2- site, O2- is bonded in a single-bond geometry to one N1- atom.

36 MATERIALS SCIENCE↗

Coupled cluster Green's function: Past, Present, and Future

Coupled cluster Green's function (CCGF) approach has drawn much attention in recent years for targeting the molecular and material electronic structure problems from a many-body perspective in a systematically improvable way. Here, we will present a brief review of the history of how the Green's function method evolved with the wavefunction, early and recent development of CCGF theory, and more recently scalable CCGF software development. We will highlight some of the recent applications of CCGF approach and propose some potential applications that would emerge in the near future.

Peng, Bo↗

Materials Data on H7ClO3 by Materials Project

H5O2H7O3ClH2OCl crystallizes in the triclinic P1 space group. The structure is zero-dimensional and consists of one hydrochloric acid, monohydrate molecule; one molecular hydrogen;dihydrate molecule; and one H7O3Cl cluster. In the H7O3Cl cluster, there are seven inequivalent H1+ sites. In the first H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 1.00 Å. In the second H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the third H1+ site, H1+ is bonded in a distorted single-bond geometry to one O2- and one Cl1- atom. The H–O bond length is 1.03 Å. The H–Cl bond length is 1.88 Å. In the fourth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the fifth H1+ site, H1+ is bonded in a distorted linear geometry to two O2- atoms. There is one shorter (1.02 Å) and one longer (1.61 Å) H–O bond length. In the sixth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 1.00 Å. In the seventh H1+ site, H1+ is bonded in a linear geometry to two O2- atoms. There is one shorter (1.14 Å) and one longer (1.29 Å) H–O bond length. There are three inequivalent O2- sites. In the first O2- site, O2- is bonded in a trigonal non-coplanar geometry to three H1+ atoms. In the second O2- site, O2- is bonded in a distorted water-like geometry to three H1+ atoms. In the third O2- site, O2- is bonded in a trigonal non-coplanar geometry to three H1+ atoms. Cl1- is bonded in a single-bond geometry to one H1+ atom.

36 MATERIALS SCIENCE↗

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗

Using Atom Dynamics to Map the Defect Structure Around an Impurity in Nano-Hematite

The geochemical cycling of actinides such as U is strongly controlled by Fe (oxyhydr)oxides. Indeed, the strong affinity of these minerals for U has factored into the design of geologic repositories for the long-term storage of nuclear waste. Many decades of work have focused on detailing the local coordination environment U associated with hematite (a-Fe2O3), a common and stable Fe(III) oxide, in order to better predict the fate and transport of U in the sub-surface. Here, for the first time, molecular dynamic simulations and atomically resolved electron microscopy of uranium atom mobility under the beam were used to map the topology of defect clusters surrounding structurally incorporated U in hematite. The ability to observe vacancies by means of metal atom hopping can be used to directly probe defects in other materials, with promising applications in geochemistry and materials science.

Ilton, Eugene S.↗

CS multitransitional study of density distribution in star-forming regions. 2: The S140 region

The S140 molecular cloud was observed in five transitions of CS with resolutions of 11 to 45 arcsec. The data were analyzed with both the LVG and microturbulent models of radiative transfer to derive the density structure. It was found that the CS emission comes from three components of gas: a spherical component centered on the infrared cluster, an arc component along the ionization front between the S140 H II region and the dense molecular cloud core, and a high-velocity component from the dense part of a molecular outflow. The spherical component contributes most to the CS emission and was analyzed in more detail than the other components. Using a temperature distribution derived from an analysis of the dust emission from S140, we fit a power-law density distribution of n(r) = n(sub i)(r/r(sub i))(exp -alpha) to the spherical component. The best fit was for n(sub i) = 1.4 x 10(exp 6) (density at r(sub i) = 0.026 pc) and alpha = 0.8. The density (n(sub i)) was found to be greater than or equal to the density required to account for the dust emission, depending on the dust opacity laws adopted. The presence of optical emission (Dinerstein, Lester, & Rank 1979) suggests a clumpy structure for the dense gas. Considerations of the virial mass and the lowest amount of column density required to produce dust emission put the volume filling factor (f(sub nu)) of the dense gas at approximately 0.14-0.5. We compared S140 with other regions of star formation where the density structure has been derived from excitation analysis. Source-source variations in density gradients and clumpiness clearly exist, ranging from alpha = 2 and f(sub nu) approximately 1 in B335 to alpha approximately 0, f(sub nu) approximately 0.1 in M17. There is a tendency for more massive star-forming regions to have a flatter density distribution, a more clumpy structure, and a large number of young stars. The implications of this tendency are discussed.

Zhou, Shudong↗

Highly Structured Water Networks in Microhydrated Dodecaborate Clusters

Here, we report a combined photoelectron spectroscopy and theoretical investigation of a series of size-selected hydrated closo-dodecaborate clusters B 12 X 12 2– ·nH 2 O (X = H, F, or I; n = 1–6). Distinct structural arrangements of water clusters from monomer to hexamer can be achieved by using different B 12 X 12 2– bases, illustrating the evident solute specificity. Because B–H···H–O dihydrogen bonds are stronger than O···H–O hydrogen bonds in water, the added water molecules are arranged in a unified binding mode by forming highly structured water networks manipulated by B 12 H 12 2– . As a comparison, the hydrated B 12 F 12 2– clusters display similar water evolution for n values of 1 and 2 but different binding modes for larger clusters, while water networks in B 12 I 12 2– share similarities with the free water clusters. This finding provides a consistent picture of the structural diversity of hydrogen bonding networks in microhydrated dodecaborates and a molecular-level understanding of microsolvation dynamics in aqueous borate chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Observation of Slow Eigen-Zundel Interconversion in H + (H 2 O) 6 Clusters upon Isomer-Selective Vibrational Excitation and Buffer Gas Cooling in a Cryogenic Ion Trap

The formation of isomers when trapping floppy cluster ions in a temperature-controlled ion trap is a generally observed phenomenon. This involves collisional quenching of the ions initially formed at high temperature by buffer gas cooling until their internal energies fall below the barriers in the potential energy surface that separate them. Here we explore the kinetics at play in the case of the two isomers adopted by the H + (H 2 O) 6 cluster ion that differ in the proton accommodation motif. One of these is most like the Eigen cation with a tricoordinated hydronium motif (denoted E), and the other is most like the Zundel ion with the proton equally shared between two water molecules (denoted Z). After initial cooling to about 20 K in the radiofrequency (Paul) trap, the relative populations of these two spectroscopically distinct isomers are abruptly changed through isomer-selective photoexcitation of bands in the OH stretching region with a pulsed (~6 ns) infrared laser while the ions are in the trap. We then monitor the relaxation of the vibrationally excited clusters and reformation of the two cold isomers by recording infrared photodissociation spectra with a second IR laser as a function of delay time from the initial excitation. The latter spectra are obtained after ejecting the trapped ions into a time-of-flight photofragmentation mass spectrometer, thus enabling long (~0.1 s) delay times. Excitation of the Z isomer is observed to display long-lived vibrationally excited states that are collisionally cooled on a ms time scale, some of which quench into the E isomer. These excited E species then display spontaneous interconversion to the Z form on a ~10 ms time scale. Furthermore, these qualitative observations set the stage for a series of experimental measurements that can provide quantitative benchmarks for theoretical simulations of cluster dynamics and the potential energy surfaces that underlie them.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Real-time equation-of-motion CC cumulant and CC Green’s function simulations of photoemission spectra of water and water dimer

Newly developed coupled-cluster (CC) methods enable simulations of ionization potentials and spectral functions of molecular systems in a wide range of energy scales ranging from core-binding to valence. This paper discusses the results obtained with the real-time equation-of-motion CC cumulant (RT-EOM-CC) approach and CC Green’s function (CCGF) approaches in applications to the water and water dimer molecules. Here, we compare the ionization potentials obtained with these methods for the valence region with the results obtained with the coupled-cluster with singles, doubles, and perturbative triples formulation as a difference of energies for N and N – 1 electron systems. All methods show good agreement with each other. They also agree well with the experiment with errors usually below 0.1 eV for the ionization potentials. We also analyze unique features of the spectral functions, associated with the position of satellite peaks, obtained with the RT-EOM-CC and CCGF methods employing single and double excitations, as a function of the monomer OH bond length and the proton transfer coordinate in the dimer. Finally, we analyze the impact of the basis set effects on the quality of calculated ionization potentials and find that the basis set effects are less pronounced for the augmented-type sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Generalized representative structures for atomistic systems

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40–72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.

atomic cluster expansion↗

Phylogenomic analysis of 589 metagenome-assembled genomes encompassing all major prokaryotic lineages from the gut of higher termites

“Higher” termites have been able to colonize all tropical and subtropical regions because of their ability to digest lignocellulose with the aid of their prokaryotic gut microbiota. Over the last decade, numerous studies based on 16S rRNA gene amplicon libraries have largely described both the taxonomy and structure of the prokaryotic communities associated with termite guts. Host diet and microenvironmental conditions have emerged as the main factors structuring the microbial assemblages in the different gut compartments. Additionally, these molecular inventories have revealed the existence of termite-specific clusters that indicate coevolutionary processes in numerous prokaryotic lineages. However, for lack of representative isolates, the functional role of most lineages remains unclear. We reconstructed 589 metagenome-assembled genomes (MAGs) from the different gut compartments of eight higher termite species that encompass 17 prokaryotic phyla. By iteratively building genome trees for each clade, we significantly improved the initial automated assignment, frequently up to the genus level. We recovered MAGs from most of the termite-specific clusters in the radiation of, for example, Planctomycetes, Fibrobacteres, Bacteroidetes, Euryarchaeota, Bathyarchaeota, Spirochaetes, Saccharibacteria, and Firmicutes, which to date contained only few or no representative genomes. Moreover, the MAGs included abundant members of the termite gut microbiota. This dataset represents the largest genomic resource for arthropod-associated microorganisms available to date and contributes substantially to populating the tree of life. More importantly, it provides a backbone for studying the metabolic potential of the termite gut microbiota, including the key members involved in carbon and nitrogen biogeochemical cycles, and important clues that may help cultivating representatives of these understudied clades.

59 BASIC BIOLOGICAL SCIENCES↗

First-Principles Simulations of CuCl in High-Temperature Water Vapor

Experimental data suggest that the solubility of copper in high-temperature water vapor is controlled by the formation of hydrated clusters of the form CuCl(H 2 O) n , where the average number of water molecules in the cluster generally increases with increasing density [Migdisov, A. A.; et al. Geochim. Cosmochim. Acta2014, 129, 33–53]. However, the precise nature of these clusters is difficult to probe experimentally. Moreover, there are some discrepancies between experimental estimates of average cluster size and prior simulation work [Mei, Y. Geofluids2018, 2018, 4279124]. Here, we have performed first-principles Monte Carlo (MC) and molecular dynamics (MD) simulations to explore these clusters in finer detail. We find that molecular dynamics is not the most appropriate technique for studying aggregation in vapor phases, even at relatively high temperatures. Specifically, our MD simulations exhibit substantial problems in adequately sampling the equilibrium cluster size distribution. In contrast, MC simulations with specialized cluster moves are able to accurately sample the phase space of hydrogen-bonding vapors. At all densities, we find a stable, slightly distorted linear H 2 O–Cu–Cl structure, which is in agreement with the earlier simulations, surrounded by a variable number of water molecules. The surrounding water molecules do not form a well-defined second solvation shell but rather a loose network of hydrogen-bonded water with molecular CuCl on the outside edge of the water cluster. We also find a broad distribution of hydration numbers, especially at higher densities. In contrast to previous simulation work but in agreement with experimental data, we find that the average hydration number substantially increases with increasing density. Moreover, the value of the hydration number depends on the choice of cluster definition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Shear Response of Ionizable Polymer Melts at the Crossover from Ionomers to Polyelectrolytes

Nonlinear shear response of polymers is affected by inherent barriers for diffusive motion, including entanglements and topology. In ionizable polymers, ionic clusters further constrain the intrinsic dynamics of the polymers, significantly enhancing their viscosity. Here, using fully atomistic molecular dynamics simulations, the nonlinear shear response of ionizable polymers is presented, across the transition from the ionomer regime where distinctive clusters dominate the structure to the polyelectrolyte regime where clusters percolate, in polystyrene randomly sulfonated with fractions of SO 3 − groups of f = 0.20 and 0.35, in pristine and tetrahydrofuran (THF) swollen polyelectrolyte melts. For f = 0.20, the ionic clusters first fracture into smaller clusters followed by splitting into individual ionic groups and eventually reform. At higher f, the clusters morph in shape but do not break under high shear. At very high shear rates, all of the chains stretch and recoil rapidly. As the shear rate is reduced, some chains stretch and recoil, while others remain largely unaffected by the shear. Macroscopically, for all systems, the shear viscosity displays initially an elastic response, followed by nonlinear shear stress overshoot and, eventually, a steady state. The evolution of viscosity with time and shear reflects that of the ionizable domains.

cluster chemistry↗

Understanding Solid Electrolyte Interphase Nucleation and Growth on Lithium Metal Surfaces

Experiments and theory are needed to decode the exact structure and distribution of components of a passivation layer formed at the anode surface of Li metal batteries, known as the Solid Electrolyte Interphase (SEI). Due to the inherent dynamic behavior as well as the lithium reactivity, the SEI structure and its growth mechanisms are still unclear. This study uses molecular simulation and computational chemistry tools to investigate the initial nucleation and growth dynamics of LiOH and Li2O that provide us with thermodynamics and structural information about the nucleating clusters of each species. Following the most favorable pathways for the addition of each of the components to a given nascent SEI cluster reveals their preferential nucleation mechanisms and illustrates different degrees of crystallinity and electron density distribution that are useful to understand ionic transport through SEI blocks.

25 ENERGY STORAGE↗

Interactions between fullerene derivatives and biological systems

Attention towards nanoparticles from the pharmaceutical and biomedical fields has significantly increased due to their attractive surface modification, high drug-loading, and improved pharmacokinetics. Fullerenes, an allotrope of carbon, stand out for their molecularly precise structure, potent radical-scavenging activity, photoactivatable reactive-oxygen species generation, and ability to definitively confine metal atoms and clusters. Accordingly, fullerene systems have been applied in various biological contexts, including increased and controlled drug delivery, antioxidative, anti-inflammatory, and photodynamic therapy, and magnetic resonance imaging. Ultimately, the pleiotropic activity of fullerenes, coupled with its precise structure and functionalization, can realize precise and tailorable medicines. Here, different from some excellent reviews focusing on the structure and chemistry of fullerene derivatives and their biomedical applications, this review highlights the interaction of fullerene materials with biological systems, with insights into their structural influence on their interactions with the cellular environment.

36 MATERIALS SCIENCE↗

Molecular Examination of Ion-Pair Competition in Alkaline Aluminate Solutions Using In Situ Liquid SIMS

Understanding the structure and composition of aluminate complexes in extremely alkaline systems such as Bayer liquors has received enormous attentions due to its fundamental and industrial importance. However, obtaining direct molecular information of the underlying ion-ion interactions using traditional approaches such as NMR or Raman spectroscopy is challenging due to the weakness of these interactions and/or their complex overlapping spectral signatures. Here, we exploit in situ liquid secondary ion mass spectrometry (SIMS) as a new approach and show how it enables new insights. In contrast with traditional techniques, using SIMS we succeeded in acquiring information on dominant ion clusters in these alkaline systems. In Na + /K + mixed alkaline aluminate solutions we clearly observe preferential formation of Na + -anion clusters over K + -anion clusters. Further, evaluation of these clusters by density functional theory (DFT) calculations shows that these structures are stable and that their relative bond energies are consistent with their observed SIMS signal intensity differences. This demonstrates a key advantage of in situ liquid SIMS for overcoming ambiguities obscuring important information in these systems on constituent molecular clusters defined by relatively weak ion pair competition and ion-solvent interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Materials Data on ScH16I3O8 by Materials Project

ScH10O7IH2OHI1IH3 crystallizes in the monoclinic P2_1/c space group. The structure is zero-dimensional and consists of four hydriodic acid molecules, four molecular hydrogen;hydroiodide molecules, four water molecules, and four ScH10O7I clusters. In each ScH10O7I cluster, Sc3+ is bonded in a 7-coordinate geometry to seven O2- atoms. There are a spread of Sc–O bond distances ranging from 1.97–2.35 Å. There are ten inequivalent H1+ sites. In the first H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the second H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the third H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the fourth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the fifth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. In the sixth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the seventh H1+ site, H1+ is bonded in a single-bond geometry to one O2- and one I1- atom. The H–O bond length is 1.00 Å. The H–I bond length is 2.42 Å. In the eighth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.99 Å. In the ninth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.97 Å. In the tenth H1+ site, H1+ is bonded in a single-bond geometry to one O2- atom. The H–O bond length is 0.98 Å. There are seven inequivalent O2- sites. In the first O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. In the second O2- site, O2- is bonded in a single-bond geometry to one Sc3+, one H1+, and one O2- atom. The O–O bond length is 1.48 Å. In the third O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. In the fourth O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. In the fifth O2- site, O2- is bonded in a 1-coordinate geometry to one Sc3+ and one O2- atom. In the sixth O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and one H1+ atom. In the seventh O2- site, O2- is bonded in a distorted water-like geometry to one Sc3+ and two H1+ atoms. I1- is bonded in a 1-coordinate geometry to one H1+ atom.

36 MATERIALS SCIENCE↗