Density Functional Theory Level Research Articles

Ultrafast Excited-State Nonadiabatic Dynamics in Pt(II) Donor-Bridge-Acceptor Assemblies: A Quantum Approach for Optical Control.

The ultrafast nonadiabatic excited state dynamics of (PTZ-N-benzyl-acetylide) (trans-bis-trimethylphosphine) Pt(II) (acetylide-NDI-bis-methyl) 1, representative of a series of Pt(II) donor-bridge-acceptor assemblies experimentally studied by the Weinstein group, University of Sheffield, is investigated by means of wavepacket propagations based on the multiconfiguration time-dependent Hartree (MCTDH) method. On the basis of electronic structure data obtained at the time-dependent density functional theory (TD-DFT) level, the subpicosecond decay is simulated by solving an 11 electronic states multimode problem, up to 18 vibrational normal modes, including both spin-orbit coupling (SOC) and vibronic coupling. A careful analysis of the results, within the diabatic representation, provides the key features of the spin-vibronic mechanism at work in this complex, distinguishing between the spin-orbit and vibronically activated ultrafast processes within the excited states manifold. The knowledge of the key active normal modes that promote selectively the population of specific electronic excited states opens a route toward optical control by selectively exciting these modes in order to drive the associated nonadiabatic processes. Relevant simulations, over 2 ps, are proposed to assess the impact of these selective vibrational excitations on the branching ratio between the primary photoproducts, namely, bridge-acceptor charge-transfer (CT) and donor-acceptor charge-separated (CS) electronic states. Whereas the excitation of the localized acetylide bridge C≡C bond stretching does not modify drastically the population of the low-lying electronic states within the first two ps, vibrational excitation of the out-of-plane twisting motion of the N-benzyl group linked to the donor entity favors the population of the 1,3CS states at the expense of the lowest 1,3CT states. This quantum study opens the route to IR optical control experiments based on the specific alteration of vibrational normal modes that activate vibronic couplings between key electronic excited states. However, the presence of critical crossings along the PES channels associated with these normal modes and the role of concurrent SOC driven ultrafast transfers of population should not be underestimated.

The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes.

The nature of ion-ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion-ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene's electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes.

Synthesis of bis-(benzhydryloxy) substituted axially silicon(IV) phthalocyanine: investigation of photophysical, photochemical, and computational electronic properties

Synthesis of axially disubstituted silicon(IV) phthalocyanine bearing benzhydryloxy groups obtained by reaction of SiPcCl2 with diphenylmethanol is reported. Characterization of the compound was performed by 1H and 13C NMR, UV–Vis, mass spectrometry, and FT-IR spectroscopy. Photophysical and photochemical properties were investigated. Fluorescence spectra measurements were performed in DMSO as solvent. A preferred chemical method was used to determine the amount of singlet oxygen production. Optimizations were calculated for the single-molecule Si-Pc with the 6–311 G (d, p) basis set at the B3LYP and B3PW91 levels of density functional theory (DFT). The charge transfer (or) charge delocalization between intramolecular donor–acceptor groups was determined by NBO analysis. Furthermore, molecular electrostatic potential, Fukui functions, electrophilic and nucleophilic character maps were calculated. The electronic structure and energy parameters of the investigated compound were determined to be ΔE calc = 2.19 eV by DFT calculation and ΔE opt = 1.84 by experimental UV spectrum.

Antiferromagnetic Chromium-Doped Tin Clusters.

The trend toward further miniaturization of micronano antiferromagnetic (AFM) spintronic devices has led to a strong demand for low-dimensional materials. The assembly of AFM clusters to produce such materials is a potential pathway that promotes studies on such clusters. In this work, we report on the discovery of the AFM Cr2Snx (x = 3-20) clusters with a stepwise growth at the density functional theory (DFT) level. In comparison, the two Cr atoms tend to stay together and be buried by Sn atoms, forming endohedral structures with one Cr atom encapsulated at size 9 and finally forming a full-encapsulated structure at size 17. Each successive cluster size is composed of its predecessor with an extra Sn atom adsorbed onto the face, giving evidence of stepwise growth. All these Cr2Snx (x = 3-20) clusters are antiferromagnets, except for the triplet-state ferrimagnetic Cr2Sn11, and all their singly negatively and positively charged ions are ferromagnets. The found stable Cr2Sn17 cluster can dimerize, yielding dimers and trimers without noticeably distorting the geometrical structure and magnetic properties of each of its constituent cluster monomers, making it possible as a building block for AFM materials.

Interactions of phosphate, phosphine oxide and phosphoramide ligands with Th(IV)

The complexation behavior of phosphate, phosphine oxide, and phosphoramide ligands with Th(IV) is investigated at the Density Functional Theory (DFT) level. Employing Energy Decomposition Analysis (EDA), the study reveals significant contributions of geometric strain and dispersion interactions in determining the stability of these complexes. It is observed that the orbital interaction in the various Th(IV) complexes increases in the order phosphate < phosphine oxide < phosphoramide.

Theoretical Evaluation of Fluorinated Resazurin Derivatives for In Vivo Applications.

Primarily owing to the pronounced fluorescence exhibited by its reduced form, resazurin (also known as alamarBlue®) is widely employed as a redox sensor to assess cell viability in in vitrostudies. In an effort to broaden its applicability for in vivo studies, molecular adjustments are necessary to align optical properties with the near-infrared imaging window while preserving redox properties. This study delves into the theoretical characterisation of a set of fluorinated resazurin derivatives proposed by Kachur et al., 2015 examining the influence of fluorination on structural and electrochemical properties. Assuming that the conductor-like polarisable continuum model mimics the solvent effect, the density functional level of theory combining M06-2X/6-311G* was used to calculate the redox potentials. Furthermore, (TD-)DFT calculations were performed with PBE0/def2-TZVP to evaluate nucleophilic characteristics, transition states for fluorination, relative energies, and fluorescence spectra. With the aim of exploring the potential of resazurin fluorinated derivatives as redox sensors tailored for in vivo applications, acid-base properties and partition coefficients were calculated. The theoretical characterisation has demonstrated its potential for designing novel molecules based on fundamental principles.

Ligand Centered Reactivity of a Transition Metal Bound Geometrically Constrained Phosphine.

The electronic properties, coordination chemistry and reactivity of metal complexes of a planar (C2v symmetric) acridane-derived geometrically constrained phosphine, P(NNN), are described. On complexation to metal centers, the phosphine was found to adopt a distorted trigonal pyramidal structure with a high barrier to pyramidal inversion (22.3 kcal/mol at 298 K for Au[P(NNN)]Cl). Spectroscopic data and theoretical calculations carried out at the density functional level of theory indicate that P(NNN) is a moderate σ-donor, with significant π-acceptor properties. Despite the distortion undergone by the phosphorus atom on coordination to metal centers, the P(NNN) ligand retains its ability to react with small molecule substrates with polar E-H bonds (MeOH, NH2Ph, NH3). It does so in a concerted fashion across one of the P-N bonds, and reversibly in the case of amine substrates. This cooperative bond activation chemistry may ultimately prove beneficial in catalysis.

Primary Antioxidant Power and Mpro SARS-CoV-2 Non-Covalent Inhibition Capabilities of Miquelianin.

The antioxidant power of quercetin-3-O-glucuronide (miquelianin) has been studied, at the density functional level of theory, in both lipid-like and aqueous environments. In the aqueous phase, the computed pKa equilibria allowed the identification of the neutral and charged species present in solution that can react with the ⋅OOH radical. The Hydrogen Atom Transfer (HAT), Single Electron Transfer (SET) and Radical Adduct Formation (RAF) mechanisms were considered, and the individual, total and fraction corrected rate constants were obtained. Potential non-covalent inhibition of Mpro from SARS-CoV-2 by miquelianin has been also evaluated.

Molecular Geometry Impact on Deep Learning Predictions of Inverted Singlet-Triplet Gaps.

We present a deep learning model able to predict excited singlet-triplet gaps with a mean absolute error (MAE) of ≈20 meV to obtain potential inverted singlet-triplet (IST) candidates. We exploit cutting-edge spherical message passing graph neural networks designed specifically for generating 3D graph representations in molecular learning. In a nutshell, the model takes as input a list of unsaturated heavy atom Cartesian coordinates and atomic numbers, producing singlet-triplet gaps as output. We exploited available large data collections to train the model on ≈40,000 heterogeneous density functional theory (DFT) geometries with available ADC(2)/cc-pVDZ singlet-triplet gaps. We ascertain the predictive power of the model from a quantitative perspective obtaining predictions on a test set of ≈14,000 molecules, whose geometries have been generated at DFT level (the same employed for the geometries in the training set), at GFN2-xTB level, and through Molecular Mechanics. We notice performance degradation upon switching to lower-quality geometries, with GFN2-xTB ones maintaining satisfactory results (MAE ≈ 50 meV on GFN2-xTB geometries, MAE ≈ 180 meV on generalized AMBER force field geometries), hinting at caution when dealing with specific chemical classes. Finally, we verify the performance of the model from the qualitative point of view, obtaining predictions on a different data set of ≈15,000 molecules already used to identify new IST molecules. We obtained predictions using both DFT and experimental X-ray geometries, with results on IST candidates similar to those provided by quantum chemical methods, with clear hints for the path toward improved performance.

Deep Potential Molecular Dynamics Study of Propane Oxidative Dehydrogenation.

Oxidative dehydrogenation (ODH) of light alkanes is a key process in the oxidative conversion of alkanes to alkenes, oxygenated hydrocarbons, and COx (x = 1,2). Understanding the underlying mechanisms extensively is crucial to keep the ODH under control for target products, e.g., alkenes rather than COx, with minimal energy consumption, e.g., during the alkene production or maximal energy release, e.g., during combustion. In this work, deep potential (DP), a neural network atomic potential developed in recent years, was employed to conduct large-scale accurate reactive dynamic simulations. The model was trained on a sufficient data set obtained at the density functional theory level. The intricate reaction network was elucidated and organized in the form of a hierarchical network to demonstrate the key features of the ODH mechanisms, including the activation of propane and oxygen, the influence of propyl reaction pathways on the propene selectivity, and the role of rapid H2O2 decomposition for sustainable and efficient ODH reactions. The results indicate the more complex reaction mechanism of propane ODH than that of ethane ODH and are expected to provide insights in the ODH catalyst optimization. In addition, this work represents the first application of deep potential in the ODH mechanistic study and demonstrates the ample advantages of DP in the study of mechanism and dynamics of complex systems.

Potential energy surfaces and dynamic properties via ab initio composite and density functional approaches.

Vibrational spectroscopy enables critical insight into the structural and dynamic properties of molecules. Presently, the majority of theoretical approaches to spectroscopy employ wavefunction-based ab initio or density functional methods that rely on the harmonic approximation. This approximation breaks down for large molecules with strongly anharmonic bonds or for molecules with large internuclear separations. An alternative to these methods involves generating molecular anharmonic potential energy surfaces (potentials) and using them to extrapolate the vibrational frequencies. This study examines the efficacy of density functional theory (DFT) and the correlation consistent Composite Approach (ccCA) in generating anharmonic frequencies from potentials of small main group molecules. Vibrational self-consistent field Theory (VSCF) and post-VSCF methods were used to calculate the fundamental frequencies of these molecules from their potentials. Functional choice, basis set selection, and mode-coupling are also examined as factors in influencing accuracy. The absolute deviations for the calculated frequencies using potentials at the ccCA level of theory were lower than the potentials at the DFT level. With DFT resulting in bending modes that are better described than those of ccCA, a multilevel DFT:ccCA approach where DFT potentials are used for single vibrational mode potentials and ccCA is used for vibrational mode-mode couplings can be utilized for larger polyatomic systems. The frequencies obtained with this multilevel approach using VCIPSI-PT2 were closer to experimental frequencies than the scaled harmonic frequencies, indicating the success of utilizing post-VSCF methods to generate more accurate representations of computed infrared spectra.

A Very Deep Graph Convolutional Network for 13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment.

Nuclear magnetic resonance (NMR) chemical shift calculations are powerful tools for structure elucidation and have been extensively employed in both natural product and synthetic chemistry. However, density functional theory (DFT) NMR chemical shift calculations are usually time-consuming, while fast data-driven methods often lack reliability, making it challenging to apply them to computationally intensive tasks with a high requirement on quality. Herein, we have constructed a 54-layer-deep graph convolutional network for 13C NMR chemical shift calculations, which achieved high accuracy with low time-cost and performed competitively with DFT NMR chemical shift calculations on structure assignment benchmarks. Our model utilizes a semiempirical method, GFN2-xTB, and is compatible with a broad variety of organic systems, including those composed of hundreds of atoms or elements ranging from H to Rn. We used this model to resolve the controversial J/K ring junction problem of maitotoxin, which is the largest whole molecule assigned by NMR calculations to date. This model has been developed into user-friendly software, providing a useful tool for routine rapid structure validation and assignation as well as a new approach to elucidate the large structures that were previously unsuitable for NMR calculations.

Efficient periodic resolution-of-the-identity Hartree-Fock exchange method with k-point sampling and Gaussian basis sets.

Simulations of condensed matter systems at the hybrid density functional theory level pose significant computational challenges. The elevated costs arise from the non-local nature of the Hartree-Fock exchange (HFX) in conjunction with the necessity to approach the thermodynamic limit. In this work, we address these issues with the development of a new efficient method for the calculation of HFX in periodic systems, employing k-point sampling. We rely on a local atom-specific resolution-of-the-identity scheme, the use of atom-centered Gaussian type orbitals, and the truncation of the Coulomb interaction to limit computational complexity. Our real-space approach exhibits a scaling that is, at worst, linear with the number of k-points. Issues related to basis set diffuseness are effectively addressed through the auxiliary density matrix method. We report the implementation in the CP2K software package, as well as accuracy and performance benchmarks. This method demonstrates excellent agreement with equivalent Γ-point supercell calculations in terms of relative energies and nuclear gradients. Good strong and weak scaling performances, as well as graphics processing unit (GPU) acceleration, make this implementation a promising candidate for high-performance computing.

Understanding the Complexation Behavior of Carbamoylphosphine Oxide Ligands with Representative f-Block Elements.

The complexation behavior of carbamoylmethylphosphine oxide ligands (CMPO), a bifunctional phosphine oxide, and their substituted derivatives with Ce(III), Eu(III), Th(IV), U(VI), and Am(III) was probed at the density functional theory (DFT) level. The enhanced extraction of trivalent rare earth elements by the 2-diphenylphosphinylethyl derivative over the conventional CMPO ligand is identified due to the availability of an additional P═O donor group in the former. In addition, the orbital and dispersive interactions play a vital role in the preference of Th(IV) over U(VI) during extraction using CMPO ligands. The better complexing ability of ligands having long alkyl chain substituents at the P atom is justified due to the observed enhanced dispersion interactions in these systems.

Substituent Effects on the Electronic Ground State (Singlet versus Triplet) of Indenyl Cations: DFT and CASPT2 Studies

AbstractThe electronic ground states of cations (singlet versus triplet) attract significant attention in the discovery of novel carbocation chemistry. Although the cyclopentadienyl cation exhibits a triplet ground state because of the antiaromaticity of the closed‐shell singlet state, the ground state of the indenyl cation, which is a benzene‐fused derivative of the cyclopentadienyl cation, is reportedly a closed‐shell singlet state rather than a triplet state. In this study, in order to establish a strategy for controlling the ground state of indenyl cations, the substituent effects at the C2 and C5 positions of their structures were computed using density functional theory and complete active space multiconfiguration second order perturbation levels of theory.

Structural versatility in nickel (II) complexes of a hydrazone ligand based on alloxan: Preparation, spectroscopic, DFT, anticancer, and molecular docking studies

New binary and mixed‐ligand nickel (II) chelates of a hyrdazone ligand (HYQX) carrying alloxan and quinoline moieties were synthesized. Spectroscopic and analytical techniques were effectively used for identifying the composition and structure of the prepared nickel (II) HYQX chelates. The neutral nature of Ni‐HYQX complexes was demonstrated by molar conductivity studies, and magnetic moment values revealed octahedral geometry. The results demonstrated that HYQX behaves as a monoanionic di‐/tridentate chelating agent in all complexes, except nitrato C2 and acetato C4 complexes, which are bi‐ and tri‐nuclear complexes, respectively, with a unique mode of coordination. The thermal decomposition patterns of Ni‐HYQX complexes were investigated in relation to structure, and thermodynamic parameters were successfully calculated. The molecular structural features of HYQX and its Ni (II) chelates were investigated based on density functional theory (DFT) level at B3LYP/6‐311G(d,p) and LanL2dz level. Ni‐HYQX complexes were found to have anticancer action against Ehrlich Ascites Carcinoma. A molecular docking investigation was conducted to determine in what way the title compounds connect to the CDK‐5 inhibitor‐crystal structure of the inhibitor EFP with CDK‐2 (PDB ID: 3IG7) in order to verify their biological activity.

Computational Modelling and Mechanistic Insight into Light‐Driven CO Dissociation of Square‐Planar Rhodium(I) Complexes

AbstractThe activation step of Vaska‐type Rh(I) complexes, such as the photocleavage of the Rh−CO bond, plays an important role in the subsequent C−H activation. To elucidate the details of the photochemistry of Vaska‐type Rh(I) complexes, such as trans‐Rh(PMe3)2(CO)(Cl), we here present a computationally derived picture as obtained at the density functional level of theory in combination with multireference wavefunction‐based methods. We have identified that the photocleavage of CO proceeds via the metal‐centered excited state (3MC, ), which is populated through intersystem crossing from the dipole‐allowed excited state S1( ‐ ). Moreover, the present study unraveled the reasons for the low C−H activation efficiency when using Rh featuring the bidentate ligand 1,2‐bis(dimethylphosphino)ethane (dmpe), namely due to its unfavorable photochemical properties, i. e., the small driving force for light‐induced CO loss and the fast deactivation of 3MC state back to the singlet ground state. In this study, we provide theoretical insight into mechanistic details underlying the light‐induced CO dissociation process, for Rh complexes featuring PMe3 and dmpe ligands.

First-Principles Anharmonic Infrared and Raman Vibrational Spectra of Materials: Fermi Resonance in Dry Ice.

We introduce a computational tool for the quantum-mechanical simulation of anharmonic infrared and Raman vibrational spectra of materials. The approach, implemented in the CRYSTAL software, stems from Taylor's expansion of the potential energy surface (PES) on the basis of normal modes up to cubic and quartic terms. The PES can be sampled with four different numerical schemes at the level of density functional theory (DFT), with local, generalized-gradient, and hybrid density functional approximations. Anharmonic states are obtained by solving Shrödinger's nuclear equation with either the vibrational self-consistent field (VSCF) or vibrational configuration interaction (VCI) methods. Nuclear quantum effects (NQEs) are thus fully accounted for. Infrared intensities are computed numerically through a Berry phase approach or analytically through a coupled-perturbed (CP) approach. Raman intensities are computed analytically via the CP approach. A variety of anharmonic features of vibrational spectra of materials can be simulated, including band shifts, combination bands, overtones, resonances (first-order Fermi, second-order Darling-Dennison), and hot bands. We showcase the effectiveness of the approach on the description of a first-order Fermi resonance (FR) in CO2 dry ice: a challenging test-case given that the FR occurs in the Raman spectrum, requires NQEs, and involves two- and three-mode couplings. Fundamental mechanistic differences with respect to the well-known FR in molecular CO2 are addressed. This application represents the first quantum-mechanical, periodic description of FR in dry ice.

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses.

An accurate and transferable machine learning (ML) potential for the simulation of binary sodium silicate glasses over a wide range of compositions (from 0 to 50% Na2O) was developed. The potential energy surface is approximated by the sum of atomic energy contributions mapped by a neural network algorithm from the local geometry comprising information on atomic distances and angles with neighboring atoms using the DeePMD code [Wang, H. Comput. Phys. Commun. 2018, 228, 178-184]. Our model was trained on a large data set of total energies and atomic forces computed at the density functional theory level on structures extracted from classical molecular dynamics (MD) simulations performed at several temperatures from 300 to 3000 K. This allows for the generation of a robust and transferable ML potential applicable over the full compositional range of glass formability at different temperatures that outperforms the empirical potentials available in the literature in reproducing structures and properties such as bond angle distribution, total distribution functions, and vibrational density of state. The generality of the approach enables the future training of a potential with other or more elements allowing for simulations of structures, properties, and behavior of ternary and multicomponent oxide glasses with nearly ab initio accuracy at a fraction of the computational cost.

Modeling and characterization of lenalidomide-loaded tripolyphosphate-crosslinked chitosan nanoparticles for anticancer drug delivery

Tripolyphosphate-crosslinked chitosan (TPPCS) nanoparticles were employed in the encapsulation of lenalidomide (LND) using a straightforward ionic cross-linking approach. The primary objectives of this technique were to enhance the bioavailability of LND and mitigate inadequate or overloading of hydrophobic and sparingly soluble drug towards cancer cells. In this context, a quantum chemical model was employed to elucidate the characteristics of TPPCS nanoparticles, aiming to assess the efficiency of these nanocarriers for the anticancer drug LND. Fifteen configurations of TPPCS and LND (TPPCS /LND1-15) were optimized using B3LYP density functional level of theory and PCM model (H2O). AIM analysis revealed that the high drug loading capacity of TPPCS can be attributed to hydrogen bonds, as supported by the average binding energy (168 kJ mol−1). The encouraging theoretical results prompted us to fabricate this drug delivery system and characterize it using advanced analytical techniques. The encapsulation efficiency of LND within the TPPCS was remarkably high, reaching approximately 87 %. Cytotoxicity studies showed that TPPCS/LND nanoparticles are more effective than the LND drug. To sum up, TPPCS/LND nanoparticles improved bioavailability of poorly soluble LND through cancerous cell membrane. In light of this accomplishment, the novel drug delivery route enhances efficiency, allowing for lower therapy doses.