A Viewpoint on Population Analyses

Population analyses have become an indispensable tool to computational chemists. Yet implementation within popular quantum chemistry software has buried the interesting philosophical choices made when partitioning the electron density into atomic contributions. There is further historical context that has significantly influenced common conceptions of chemical bonding and reactivity. This work reviews select aspects of orbital and spatial decomposition schemes of the density matrix, pointing out essential linear algebraic considerations and associated tools of shared interest to us and Prof. Mayer.

Unravelling the Interactions of Magnetic Ionic Liquids by Energy Decomposition Schemes: Towards a Transferable Polarizable Force Field

This work aims at unravelling the interactions in magnetic ionic liquids (MILs) by applying Symmetry-Adapted Perturbation Theory (SAPT) calculations, as well as based on those to set-up a polarisable force field model for these liquids. The targeted MILs comprise two different cations, namely: 1-butyl-3-methylimidazolium ([Bmim]+) and 1-ethyl-3-methylimidazolium ([Emim]+), along with several metal halides anions such as [FeCl4]−, [FeBr4]−, [ZnCl3]− and [SnCl4]2− To begin with, DFT geometry optimisations of such MILs were performed, which in turn revealed that the metallic anions prefer to stay close to the region of the carbon atom between the nitrogen atoms in the imidazolium fragment. Then, a SAPT study was carried out to find the optimal separation of the monomers and the different contributions for their interaction energy. It was found that the main contribution to the interaction energy is the electrostatic interaction component, followed by the dispersion one in most of the cases. The SAPT results were compared with those obtained by employing the local energy decomposition scheme based on the DLPNO-CCSD(T) method, the latter showing slightly lower values for the interaction energy as well as an increase of the distance between the minima centres of mass. Finally, the calculated SAPT interaction energies were found to correlate well with the melting points experimentally measured for these MILs.

Strength and Nature of Host-Guest Interactions in Metal-Organic Frameworks from a Quantum-Chemical Perspective

<div> <div> <div> <p>Metal-organic frameworks offer a convenient means for capturing, transporting, and releasing small molecules. Rational design of such systems requires an in-depth understanding of the underlying non-covalent interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. Using experimentally characterised complexes of calcium-adipate framework with 4,4’-bipyridine and 1,2-bis(4-pyridyl)ethane guests as test systems, we have assessed a range of density functional theory methods, energy decomposition schemes, and non-covalent interactions indicators across realistic periodic and finite supramolecular cluster scales. We find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost and with an added benefit of diverse density partitioning schemes. Host-guest interaction energies can be reliably computed with dispersion- corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools beyond local bonding indices (e.g., the quantum theory of atoms in molecules), such as the non-covalent interactions index and the density overlap regions indicator. </p> </div> </div> </div>

Strength and Nature of Host-Guest Interactions in Metal-Organic Frameworks from a Quantum-Chemical Perspective

<div> <div> <div> <p>Metal-organic frameworks offer a convenient means for capturing, transporting, and releasing small molecules. Rational design of such systems requires an in-depth understanding of the underlying non-covalent interactions, and the ability to easily and rapidly pre-screen candidate architectures in silico. In this work, we devised a recipe for computing the strength and analysing the nature of the host-guest interactions in MOFs. Using experimentally characterised complexes of calcium-adipate framework with 4,4’-bipyridine and 1,2-bis(4-pyridyl)ethane guests as test systems, we have assessed a range of density functional theory methods, energy decomposition schemes, and non-covalent interactions indicators across realistic periodic and finite supramolecular cluster scales. We find that appropriately constructed clusters readily reproduce the key interactions occurring in periodic models at a fraction of the computational cost and with an added benefit of diverse density partitioning schemes. Host-guest interaction energies can be reliably computed with dispersion- corrected density functional theory methods; however, decoding their precise nature demands insights from energy decomposition schemes and quantum-chemical tools beyond local bonding indices (e.g., the quantum theory of atoms in molecules), such as the non-covalent interactions index and the density overlap regions indicator. </p> </div> </div> </div>

A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameter-Based Unsupervised Classification Scheme Using a Geodesic Distance

© 1980-2012 IEEE. We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a geodesic distance between pairs of 4× 4 real Kennaugh matrices. In standard model-based decomposition schemes, the 3× 3 Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the geodesic distance (GD) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter (αGD), helicity parameter (τ GD), and purity parameter (PGD). A PGD/αGD unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.

A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameter-Based Unsupervised Classification Scheme Using a Geodesic Distance

© 1980-2012 IEEE. We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a geodesic distance between pairs of 4× 4 real Kennaugh matrices. In standard model-based decomposition schemes, the 3× 3 Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the geodesic distance (GD) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter (αGD), helicity parameter (τ GD), and purity parameter (PGD). A PGD/αGD unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.

A unified diagnostic platform to quantify the source of uncertainty in modelling global SOC dynamics

&lt;p&gt;As the largest carbon reservoir in biosphere, soil organic carbon (SOC) has been extensively studied. However, the large uncertainty of modeling SOC &amp;#160;impedes the accurate prediction of global carbon dynamics in response to climate change. Thus, evaluating and tracing the sources of large uncertainty in predicting SOC dynamics by Earth system models are the urgently needed to improve our understanding and predicting capability. Although great efforts have been made to predict land C storage using multiple models, disentangle uncertainty sources among models are still extremely difficult. To take this challenge, we developed a Matrix-based ensemble Model Inter-comparison Platform (MeMIP). MeMIP is an integrated platform to quantify the various sources of uncertainty under a unified framework. MeMIP is embedded a new community-based ESM, Community Integrated Earth System Model (CIESM) and implemented in the super-computing cluster in Wuxi, China. Within the MeMIP, multiple SOC decomposition schemes from different land models (e.g. CLM-CENTURY, CLM-BGC, LPJ-GUESS, JULES and CABLE) have been re -constructed in a unified matrix model format. With the unified format of matrix model, the inter-model differences can be quantitatively attributed to the sources by using the traceability analysis. In this study, we analyzed how SOC decomposition schemes and the vertical resolved SOC exchange structure (VR structure) influences SOC prediction with the three-dimension parameter output (NPP, residence time and carbon storage potential) space. The results indicate that model with the VR structure result in significantly higher SOC predictions and introduced higher uncertainty than single layer models. It is mainly due to increased residence time, which is also very sensitive to future warming. The identified major uncertain components are targets for improvement via data assimilation. Overall, MeMIP provides a modeling platform that not only unifies all land carbon cycle models in the matrix form but also offers traceability analysis to identify sources of uncertainty and data assimilation to constrain multiple model ensemble prediction.&lt;/p&gt;