Density Descriptors Research Articles

Investigating the impact of acetylenic linkage in C20 fullerene: Utilization in optoelectronic devices and as anode material

This study delves into the diverse characteristics of C80 fulleryne, derived from the incorporation of acetylenic linkages into each chain of the smallest fullerene, C20. Utilizing advanced theoretical computations, we confirm the stability of C80 fulleryne. Notably, its possession of a finite energy gap (0.743 eV) categorizes it as a semiconductor. Further enlightenment into its structural stability, bonding and reactivity stems from an exploration of different energy components, topological descriptors of electron density and chemical reactivity parameters. C80 fulleryne can act as radical scavenger owing to its higher electron affinity (5.5 eV). Spectral analysis revealed the C80 fulleryne’s absorption within the near-infrared region and its intriguing optical transparency with negligible loss in specific regions of the electromagnetic spectrum. Additionally, our investigation scrutinized the viability of C80 fulleryne as an anode material in Lithium-ion batteries. The presence of low adsorption energy (−1.693 eV) and significant amount of Bader charge transfer (0.917 e) between Li-ion and C atoms of C80 fulleryne confirms that Li-ion gets chemisorbed on the C80 fulleryne. Remarkably, this cage acts as a storehouse to host 12 Li ions, precisely one for each of its constituent pentagons within the C80 fulleryne structure, bearing a theoretical capacitance of 335 mAhg−1.

β-d-Glucopyranose – Silver+ (1:1) complex assisted aromaticity involving electron deficient X3 (X=Be, Mg) core

Seeing the frontier molecular orbitals (FMO) picture of β-d-glucopyranose–silver+ (1:1) complex ([Ag(C6H12O6)]+) reported in a recent article (Mondal, 2024) [53], we used the FMO based approach to obtain inverted sandwich like organometallic complex comprising X3 (X=Be, Mg) core, in silico. Thermodynamic feasibility of obtaining [(C6H12O6)Ag-X3-Ag(C6H12O6)] (X=Be, Mg) complexes are explored. Conceptual density functional theory (CDFT) based reactivity descriptors guide to ascertain the stability order of such complexes. By the help of natural bond orbital analysis, Wiberg bond indices (WBI), bond critical point analysis, electron density descriptors, and gradient isosurface between selected contacts, the nature of interaction between the X3 core and Ag metals are studied. FMO of the complexes further help in ascertaining the nature of binding. Energy decomposition analysis marks the interaction between X3 and Ag metals in [(C6H12O6)Ag-X3-Ag(C6H12O6)] (X=Be, Mg) as partially covalent. Negative values of nucleus independent chemical shift (NICS) at the triangular ring (X3) centres [NICS(0)] indicate aromatic property.

Monitoring Density Redistribution at the Excited State in a Dual Emitting Molecule: An Analysis Based on Real-Time Density Functional Theory and Density Descriptors.

In this work, we computed and analyzed, by means of density-based descriptors, the real-time evolution of both the locally excited (LE) and charge-transfer (CT) excited states for the planar and twisted conformations of the DMABN (4-(N,N-dimethylamino)benzonitrile) molecule using real-time time-dependent density functional theory (DFT) and three different exchange-correlation energy functionals (EXC) belonging to the same family (the PBE one). Our results based on the analysis of density-based descriptors show that the underlying EXC modifies the evolution in time of the density. In particular, comparing the frequency of density reorganization computed with the three functionals (PBE, PBE0, and LC-PBE), we found that the frequency of electronic interconversion of the individual determinants involved during the dynamics increases from PBE to PBE0 and to LC-PBE. This allows us to show that there is a correlation between the delocalization of the electronic density and the frequency of reorganization. In particular, the greater the mean hole-electron distance during the dynamics, the lower is the frequency of density reorganization.

β-D-Glucopyranose-silver+ (1:1) complex as a small gas molecule scavenger.

In this article, density functional theory computations at the PBE0-D3/def2-TZVP level are reported to unveil the type of bonding between β-D-glucopyranose-silver ion (1:1) complex ([Ag(C6H12O6)]+) and seven gas molecules, namely, H2, C2H2, C2H4, CO, N2, NO and O2. Moreover, the relative preference of trapping among these molecules within the sight of Ag metal ion in the complex is explored. The nature of interaction of these small molecules with the [Ag(C6H12O6)]+ ion is studied. Exergonic nature of binding is noted with the metal center for all the chosen small molecules except O2. Thermochemical data reveals the binding preference of C2H4 > C2H2 > CO > NO > N2 > H2. Natural bond orbital analysis, contour plot of the Laplacian of electron density, electron density descriptors, and gradient isosurface help in understanding the nature of interactions. Maximum bond formation is noted between the Ag-complex and CO molecule. Assessed energy decomposition analysis discloses the nature of interaction as mainly orbital between the bound small gas molecules and the Ag-complex. Frontier molecular orbital pictures further help in understanding the type of interaction as orbital. To disclose the kinetic stability of the gas molecule bound Ag complexes an ab initio molecular dynamics study is done at different temperatures up to 2 ps. These studies help in understanding the type of adsorption. Calculated conceptual density functional theory (CDFT) based reactivity descriptors corroborate well with results. β-D-glucopyranose-silver ion (1:1) complex may be used as small gas molecule scavenger.

Machine Learning Density Functionals from the Random-Phase Approximation.

Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we use machine learning to map the RPA to a pure Kohn-Sham density functional. The machine learned RPA model (ML-RPA) is a nonlocal extension of the standard gradient approximation. The density descriptors used as ingredients for the enhancement factor are nonlocal counterparts of the local density and its gradient. Rather than fitting only RPA exchange-correlation energies, we also include derivative information in the form of RPA optimized effective potentials. We train a single ML-RPA functional for diamond, its surfaces, and liquid water. The accuracy of ML-RPA for the formation energies of 28 diamond surfaces reaches that of state-of-the-art van der Waals functionals. For liquid water, however, ML-RPA cannot yet improve upon the standard gradient approximation. Overall, our work demonstrates how machine learning can extend the applicability of the RPA to larger system sizes, time scales, and chemical spaces.

Decoding Chemical Bonds: Assessment of the Basis Set Effect on Overlap Electron Density Descriptors and Topological Properties in Comparison to QTAIM.

Quantum chemical bonding descriptors based on the total and overlap density can provide valuable information about chemical interactions in different systems. However, these descriptors can be sensitive to the basis set used. To address this, different numerical treatments of electron density have been proposed to reduce the basis set dependency. In this work, we introduce overlap properties (OPs) obtained through numerical treatment of the electron density and present the topology of overlap density (TOP) for the first time. We compare the basis set dependency of numerical OP and TOP descriptors with their quantum theory of atoms in molecules (QTAIM) counterparts, considering the total electron density. Three single (C-C, C-O, and C-F) bonds in ethane, methanol, and fluoromethane and two double (C═C and C═O) bonds in ethene and formaldehyde were analyzed. Diatomic molecules Li-X with X = F, Cl, and Br were also analyzed. Eight parameters, including QTAIM descriptors and OP/TOP descriptors, are used to assess the basis dependency at the ωB97X-D level of theory using 28 basis sets from three classes: Pople, Ahlrichs, and Dunning. The study revealed that the topological overlap electron density properties exhibit comparatively lesser dependence on the basis set compared to their total electron density counterparts. Remarkably, these properties retain their chemical significance even with reduced basis set dependency. Similarly, numerical OP descriptors show less basis set dependency than their QTAIM counterparts. The excess of polarization functions increases charge concentration in the interatomic region and influences both QTAIM and OP descriptors. The basis sets Def2TZVP, 6-31++G(d,p), 6-311++G(d,p), cc-pVDZ, cc-pVTZ, and cc-pVQZ demonstrate reduced variability for the tested bond classes in this study, with particular emphasis on the triple-ζ quality Ahlrichs' basis set. We recommend against using basis sets with numerous polarization functions, such as augmented Dunning's and Ahlrichs' quadruple-ζ.

Modeling and characterization of the nucleation and growth of carbon nanostructures in physical synthesis

Carbon nanostructures formed through physical synthesis come in a variety of sizes and shapes. With the end goal of rationalizing synthetic pathways of carbon nanostructures as a function of tunable parameters in the synthesis, we investigate how the initial density and quench rate influence the morphology of carbon nanostructures obtained from the cooling of a gas of atomic carbon by molecular dynamics simulations. For the structural analysis, we combine classical order parameters with a data-driven approach based on local density descriptors and kernel similarity measurements. Aided by this complementary set of tools, we describe in detail the formation of carbon nanostructures from the gas phase. Their formation proceeds through the nucleation of small liquid carbon nanoclusters followed by growth into unique objects. We find that ordered structures can only be obtained at certain quenching rates and that among those, fullerene-like particles are favored at intermediate densities while nanotubes-like structures require higher initial densities. © XXXX CEA. Elsevier

A semilocal machine-learning correction to density functional approximations.

Machine learning (ML) has demonstrated its potential usefulness for the development of density functional theory methods. In this work, we construct an ML model to correct the density functional approximations, which adopts semilocal descriptors of electron density and density derivative and is trained by accurate reference data of relative and absolute energies. The resulting ML-corrected functional is tested on a comprehensive dataset including various types of energetic properties. Particularly, the ML-corrected Becke's three parameters and the Lee-Yang-Parr correlation (B3LYP) functional achieves a substantial improvement over the original B3LYP on the prediction of total energies of atoms and molecules and atomization energies, and a marginal improvement on the prediction of ionization potentials, electron affinities, and bond dissociation energies; whereas, it preserves the same level of accuracy for isomerization energies and reaction barrier heights. The ML-corrected functional allows for fully self-consistent-field calculation with similar efficiency to the parent functional. This study highlights the progress of building an ML correction toward achieving a functional that performs uniformly better than B3LYP.

An electronic structure investigation of excited state intramolecular proton transfer in amino-benzazole derivatives: Relative energies and electron density descriptors

An electronic structure study of the excited-state intramolecular proton transfer (ESIPT) process of six amino-benzazole derivatives (4-AHBI, 4-AHBO, 4-AHBT, 5-AHBI, 5-AHBO, and 5-AHBT) based on Density Functional Theory (DFT) is carried out in gas phase and also in dichloromethane. The energies, geometries and electronic excitations are initially evaluated, being followed by electron density descriptor investigations done to provide a better understanding of the hydrogen bond (HB) strength of enol (N···H–O), EI, and keto (O···H–N), KI, forms of these molecules. First, one can notice that the solvent effect is fundamental to explain the ESIPT process in 5-AHBO and 5-AHBT due partially to a covalent character increment of these N–H bonds in KI, while gas phase results obtained in the first excited state indicate that the enol tautomer is instead more stable than the keto one. The time-dependent DFT (TDDFT) values achieved support a reevaluation of the experimental data available, showing that ESIPT is really observed for all these compounds in dichloromethane. The most important effect of the solvent on HBs seems to be a destabilization of O···H interactions in the KI form, which is observed in both the electronic states (S0 and S1). The electronic excitation also appears to induce a weakening of O···H bonds in the KI tautomer along with some strengthening of N···H interactions in EI. Thus, this HB force variation profile does not favor the ESIPT occurrence and one must consider instead the effect of changes in the covalent bonds on excitation to understand why the KI tautomer becomes more stable than EI in the S1 state, such as supported by evidence regarding the weakening of the O–H bonds and the strengthening of the N–H ones driven by the electronic transition.

The prediction and assessment of properties for high energy density fuel – Adamantane derivatives: The combined DFT and molecular dynamics simulation

The structures of high-energy–density fuel are significant factors to influence property, which in turn determines the flight distance and payload of vehicles. The density functional theory and molecular dynamics simulation are employed to predict properties of adamantane derivatives, and explore the effect of structure with the different substitutes and the isomers on properties including density, net heat value, specific impulse, thermal and oxidation stability. For adamantane derivatives with different substitutes, the C/H ratio is treated as the descriptors of density and net heating value. While the number of secondary carbon atom attached by side chains and the gap between HOMO and LUMO are viewed as the descriptors of density and volumetric net heating value for isomers, respectively. Based on the above descriptor, the structures with superior properties are obtained for ethyladamantane, propyladamantane and butyladamantane. The structure of pentyladamantane with excellent properties (2c-ethyl-2t,4c,4t-trimethyladamantane) is predicted due to resembling structure of adamantane derivatives with preferable properties. The thermal and oxidation stability of 2c,2t-dimethyladamantane, 2c-ethyl-2t-methyladamantane, 2c,2t-diethyladamantane and 2c-ethyl-2t,4c,4t-trimethyladamantane with superior properties is close to tetrahydro-dicyclopentadiene meeting requirement of aircraft. Therefore, the guidance, that predicts, screens and assesses the structure and property of adamantane derivatives, is provided in this study by defining descriptors.

Theoretical Insight into B–C Chemical Bonding in Closo-Borate [BnHn−1CH3]2− (n = 6, 10, 12) and Monocarborane [CBnHnCH3]− (n = 5, 9, 11) Anions

A theoretical investigation of mono-methyl derivatives of closo-borate anions of the general form [BnHnCH3]2– (n = 6, 10, 12) and monocarboranes [HCBnHnCH3]− (n = 5, 9, 11) was carried out. An analysis of the main bonding descriptors of exo-polyhedral B–C bonds was performed using the QTAIM (quantum theory of “Atoms in Molecules”), ELF (electron localisation function), NBOs (natural bond orbitals) analyses and several other approaches for the estimation of B–C bond orders (viz. Laplacian bond order (LBO), fuzzy bond order (FBO) and Mayer and Wiberg formalisms). Based on the data obtained on electron density descriptors, it can be concluded that orbital interaction increases with increasing boron cluster size. The present investigation provides a better understanding of exo-polyhedral B–C bond phenomena in boron cluster systems. The data obtained can be used to estimate B–C bond strength, which can be useful for studies devoted to the synthesis and properties of boron cluster systems.

Simple machine-learned interatomic potentials for complex alloys

Developing data-driven machine-learning interatomic potential for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperforming the high-dimensional SOAP descriptor in data efficiency, accuracy, and speed. In particular, we develop a computationally fast machine-learned and tabulated Gaussian approximation potential (tabGAP) for Mo--Nb--Ta--V--W alloys with a combination of two-body, three-body, and a new simple scalar many-body density descriptor based on the embedded atom method.

A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration

The co-registration between SAR and optical images is a challenging task because of the speckle noise of SAR and the nonlinear radiation distortions (NRD), particularly in the one-look situation. In this paper, we propose a novel density descriptor based on the histogram of oriented primary edge structure (HOPES) for the co-registration of SAR and optical images, aiming to describe the shape structure of patches more firm. In order to extract the primary edge structure, we develop the novel multi-scale sigmoid Gabor (MSG) detector and a primary edge fusion algorithm. Based on the HOPES, we propose the co-registration method. To obtain stable and uniform keypoints, the non-maximum suppressed SAR-Harris (NMS-SAR-Harris) and deviding grids methods are used. NMS-SSD fast template matching and fast sample consensus (FSC) algorithm are used to further complete and optimize matching. We use two one-look simulated SAR images to demonstrate that the signal-to-noise ratio (SNR) of MSG is more than 10 dB higher than other state-of-the-stage detectors; the binary edge maps and F-score show that MSG has more accurate positioning performance. Compared with the other state-of-the-stage co-registration methods, the image co-registration results obtained on seven pairs of test images show that, the correct match rate (CMR) and the root mean squared error (RMSE) improve by more than 25% and 15% on average, respectively. It is experimentally demonstrated that the HOPES is robust against speckle noise and NRD, which can effectively improve the matching success rate and accuracy.

Posidonia oceanica L. (Delile) meadows regression: Long-term affection may be induced by multiple impacts

Coastal development has an undeniable impact on marine ecosystems resulting in the detriment of the more sensible communities. Posidonia oceanica meadows are climax communities which offer a wide variety of ecosystem services both ecological and socio-economic. Human-derived impact on these habitats has been widely assessed although conclusions may vary depending on the area. P. oceanica meadow regression next to the city of Alicante (SE Spain) was analyzed on the long term (1984–2014) using bionomic cartographies and side-scan sonar images and, during the last two decades (2003–2021), using cover percentage and shoot density descriptors in the remaining meadow. Results showed a 25% colonized area reduction since 1984, this process being more rapid during the 1984–1994 period and decreasing with time. Cover and density have suffered a significant decrease in the last 20 years, mainly in the upper limit of the meadow. Dead matte cover was also assessed and have shown a significant increase in the same period following an inverse trend with the other metrics. There are several coastal impacts which have co-occurred in the area in the last few decades (port enlargement, brine and sewage discharges, industrial activity) thus resulting in the regression of the meadow. The existing negative trend of the measured descriptors indicate the necessity of implementing management actions which focus on the present sources of impact and actively reduce their effect on P. oceanica beds.

Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors.

A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.

Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality

Recent advances in machine-learned interatomic potentials largely benefit from the atomistic representation and locally invariant many-body descriptors. It was, however, recently argued that including three-body (or even four-body) features is incomplete to distinguish specific local structures. Utilizing an embedded density descriptor made by linear combinations of neighboring atomic orbitals and realizing that each orbital coefficient physically depends on its own local environment, we propose a recursively embedded atom neural network model. We formally prove that this model can efficiently incorporate complete many-body correlations without explicitly computing high-order terms. This model not only successfully addresses challenges regarding local completeness and nonlocality in representative systems, but also provides an easy and general way to update local many-body descriptors to have a message-passing form without changing their basic structures.

Substituent Effects on Electride Characteristics of Mg2(η5-C5H5)2: A Theoretical Study.

An ab initio study has been carried out on the substituted binuclear sandwich complexes of Mg2(η5-C5H5)2. We have checked whether the substitution destroys the electride properties of a complex, as it needs to satisfy several stringent criteria to obtain the status of an electride. The thermochemical results show that the complexes are stable at room temperature and 1 atm pressure. From the analysis of the various electron density descriptors and the natural bond orbital (NBO) for all the complexes, it is confirmed that the Mg-Mg bonds are covalent and the metal-ligand bonds are ionic in nature. The charges on each Mg atom in the studied complexes are +1. Analysis of the electron density descriptors shows the presence of a non-nuclear attractor (NNA) at the middle of the bond formed by the two Mg atoms when attached to the ligands. The electride characteristics are exhibited by all of the designed complexes. We also report the aromaticity behavior and reactivity descriptors of these complexes. The electride characteristics of Mg2(η5-C5H5)2 complex get affected on substitution, as both the NNA population and the nonlinear optical properties (NLO) of the complexes are changed.

Electride characteristics of M2(η5-E5)2 (M = Be, Mg; E = Sb5-)

Ab initio computation is performed on the binuclear sandwich complexes, M2(η5-Sb5)2. Eclipsed and staggered conformations are generated due to the η5 mode of binding by Sb5- ligand with the alkaline earth metals (Be and Mg metals). The complexes are thermodynamically stable at room temperature. The electron density descriptors and the natural bond orbital (NBO) analysis confirmed the covalent nature of the M-M bond. Both Be2(η5-Sb5)2 and Mg2(η5-Sb5)2 complexes have one non-nuclear attractor (NNA) at the center of the M-M bond which is predicted and confirmed by the electron density analysis. At the NNA, the values of the Laplacian of electron density are negative and an electron localization function basin (ELF) is present at the center of the M-M bond for localized electrons. Both the complexes show large values of nonlinear optical (NLO) properties. Both the designed binuclear sandwich complexes Be2(η5-Sb5)2 and Mg2(η5-Sb5)2 behave as electride.

Boronic Acids as Prospective Inhibitors of Metallo-β-Lactamases: Efficient Chemical Reaction in the Enzymatic Active Site Revealed by Molecular Modeling.

Boronic acids are prospective compounds in inhibition of metallo-β-lactamases as they form covalent adducts with the catalytic hydroxide anion in the enzymatic active site upon binding. We compare this chemical reaction in the active site of the New Delhi metallo-β-lactamase (NDM-1) with the hydrolysis of the antibacterial drug imipenem. The nucleophilic attack occurs with the energy barrier of 14 kcal/mol for imipenem and simultaneously upon binding a boronic acid inhibitor. A boron atom of an inhibitor exhibits stronger electrophilic properties than the carbonyl carbon atom of imipenem in a solution that is quantified by atomic Fukui indices. Upon forming the prereaction complex between NDM-1 and inhibitor, the lone electron pair of the nucleophile interacts with the vacant p-orbital of boron that facilitates the chemical reaction. We analyze a set of boronic acid compounds with the benzo[b]thiophene core complexed with the NDM-1 and propose quantitative structure-sroperty relationship (QSPR) equations that can predict IC50 values from the calculated descriptors of electron density. These relations are applied to classify other boronic acids with the same core found in the database of chemical compounds, PubChem, and proposed ourselves. We demonstrate that the IC50 values for all considered benzo[b]thiophene-containing boronic acid inhibitors are 30–70 μM.

Efficient selection of linearly independent atomic features for accurate machine learning potentials

Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost. However, the reliability, speed, and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented. A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation. Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates, based on the correlations that are intrinsic to the training data. Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors, we demonstrate the efficiency and accuracy of this new strategy. The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.