Chemical bonding in crystals: new directions

Gatti, Carlo

doi:10.1524/zkri.220.5.399.65073

Cited by 619 publications

(521 citation statements)

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“…The characterizing features [71,80,81] of QTAIM were employed to determine whether the nickel-oxide solid interactions were of shared or closed-shell nature. The calculated topological properties are shown in Table 4.…”

Section: Resultsmentioning

confidence: 99%

Theoretical identification of structural heterogeneities of divalent nickel active sites in NiMCM-41 nanoporous catalysts

2016

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This paper deals with the theoretical identification of the digrafted Ni species exchanged into the defect sites of MCM-41 using hybrid density functional theory. The nickel-siloxane clusters included seven 2T-6T rings. The 2MR and 5MR structures were found to be the least and most favorable sites to form thermodynamically. The Ni-O distances ranged from 1.69 to 1.79 Å with the highest asymmetry found in 5MR. The 4MR and 5MR clusters showed also interesting intertwined nickel configurations. Overall, the QTAIM calculations revealed the transient electrostatic nature of the Ni-O bonds.

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Section: Resultsmentioning

confidence: 99%

Theoretical identification of structural heterogeneities of divalent nickel active sites in NiMCM-41 nanoporous catalysts

2016

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“…Once again, we assume these to be non-degenerate, which means that they will take on the form of single Slater determinants 16) where the single-particle orbitals φ i satisfy 17) and each ε i is a KS eigenvalue. The density and kinetic energy are now readily obtained as…”

Section: The Kohn-sham Non-interacting Systemmentioning

confidence: 99%

“…It has also been applied to the study of chemical bonds in molecular and solid state systems. 54,16 The ELF is defined by…”

Section: The Electron Localization Functionmentioning

confidence: 99%

Theoretical prediction of properties of atomistic systems : Density functional theory and machine learning

Lindmaa¹

2017

Linköping Studies in Science and Technology. Dissertations

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The prediction of ground state properties of atomistic systems is of vital importance in technological advances as well as in the physical sciences. Fundamentally, these predictions are based on a quantum-mechanical description of many-electron systems. One of the hitherto most prominent theories for the treatment of such systems is density functional theory (DFT). The main reason for its success is due to its balance of acceptable accuracy with computational efficiency. By now, DFT is applied routinely to compute the properties of atomic, molecular, and solid state systems.The general approach to solve the DFT equations is to use a densityfunctional approximation (DFA). In Kohn-Sham (KS) DFT, DFAs are applied to the unknown exchange-correlation (xc) energy. In orbitalfree DFT on the other hand, where the total energy is minimized directly with respect to the electron density, a DFA applied to the noninteracting kinetic energy is also required. Unfortunately, central DFAs in DFT fail to qualitatively capture many important aspects of electronic systems. Two prime examples are the description of localized electrons, and the description of systems where electronic edges are present.In this thesis, I use a model system approach to construct a DFA for the electron localization function (ELF). The very same approach is also taken to study the non-interacting kinetic energy density (KED) in the slowly varying limit of inhomogeneous electron densities, where the effect of electronic edges are effectively included. Apart from the work on model systems, extensions of an exchange energy functional with an improved KS orbital description are presented: a scheme for improving its description of energetics of solids, and a comparison of its description of an essential exact exchange feature known as the derivative discontinuity with numerical data for exact exchange.An emerging alternative route towards the prediction of the properties of atomistic systems is machine learning (ML). I present a number of ML methods for the prediction of solid formation energies, with an accuracy that is on par with KS DFT calculations, and with orders-ofmagnitude lower computational cost. V Svensk sammanfattningAtt kunna förutsäga egenskaper hos atomistiska system utgör en viktig del av vår teknologiska utveckling, samt spelar en betydande roll i de fysikaliska vetenskaperna. Sådana förutsägelser bygger på en kvantmekanisk beskrivning av mångelektronsystem. En av de mest framstående teorierna för att behandla den här typen av system är täthets-funktionalteorin (DFT). Den främsta orsaken till dess framgång är att den lyckas kombinera skaplig noggrannhet med en bra beräkningsef-fektivitet. DFT används numera rutinmässigt för att beräkna storheter hos atomer, molekyler, och fasta kroppar.Generellt sett löses ekvationerna inom DFT genom att man inför en täthetsfunktionalapproximation (DFA). I Kohn-Sham (KS) DFT, används DFAer för att approximera utbytes-korrelationsenergin. Inom orbitalfri DFT, där målet är att direkt minimera den totala ...

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“…If the outcomes of these statistical or correlation analyses help us to control, e.g., substituent effects in a chemical system, the behaviour of materials in different compositions, crystal packing effects, reactivity etc. (and there are numerous examples for successful applications of topological analysis in these different fields [47,[57][58][59][60][61][62][63][64][65][66][67][68][69]), then it doesn't really matter how deep the philosophical connection to well-defined or weak chemical concepts is. In other words, we don't need to find justifications for the use of topological analysis if it proves to be helpful for a certain application or chemical question.…”

Section: ±3mentioning

confidence: 99%