Density Functional Tight Binding Research Articles

Efficient Parameterization of Density Functional Tight-Binding for 5f-Elements: A Th-O Case Study.

Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th-O system, which was subsequently applied to the study of ThO2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO2. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.

Implementation of Nonadiabatic Molecular Dynamics for Intersystem Crossing Based on a Time-Dependent Density-Functional Tight-Binding Method.

Intersystem crossing (ISC) and internal conversion (IC) are types of nonadiabatic transitions that play important roles in a wide range of fields, including photochemistry, photophysics, and photobiology. The nonadiabatic molecular dynamics (NA-MD) method is a powerful tool for computational simulations of dynamic phenomena involving nonadiabatic transitions. In this study, we implemented the NA-MD method, which treats ISC and IC on an equal footing, where the electronic structure is treated at the level of the time-dependent (TD) density-functional tight-binding (DFTB) method, a low-cost semiempirical analog of TD density functional theory (DFT). In particular, the spin-orbit coupling calculation algorithm was implemented in the TD-DFTB framework, and the results showed trends similar to those obtained using TD-DFT. In addition, the NA-MD method successfully reproduced ultrafast ISC of 2-nitronaphthalene.

Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models.

Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4'-bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals.

Amelioration of protective organic layer using acenaphthene-based inhibitor responsible for excellent anti-corrosion performance: Experimental and computational perspectives

Despite the engineering promise offered by the co-existence of organic substances in protecting vulnerable metallic materials from corrosive environments, the interaction and formation mechanisms that induce the development of new materials with improved structural properties remain less understood. This study investigated the corrosion inhibitory properties of two acenaphthene derivatives, acenaphthylene-1,2‑dione (ACP) and 1,2-dihydroacenaphthylen-1-ol (DCP), as inhibitors for N80 steel in a 15 % HCl solution. The anticorrosive effectiveness of ACP and DCP compounds was evaluated through multiple research methods, including weight loss analysis, electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), and surface characterization using scanning electron microscopy (SEM). The experimental findings demonstrate that ACP exhibits superior inhibition performance compared to DCP, with an inhibition efficiency of 93.54 % for ACP and 73.12 % for DCP at an optimum concentration of 5 × 10–3 M. EIS data show that the charge transfer resistance increases with higher concentrations of both compounds. PDP results indicate that ACP and DCP act as mixed-type inhibitors without altering the corrosion mechanism. The kinetic parameters of corrosion adsorption follow the Langmuir isotherm, highlighting a competition between physical and chemical interactions. To investigate how the geometric structures resulting from functional groups influence their interaction with the metal surface, theoretical calculations employing density functional theory (DFT), molecular dynamics (MD), and density-functional tight-binding (DFTB) were conducted. These calculations were aimed at gaining insights into the adsorption behavior of ACP and DCP on the metal surface and their interfacial mechanism. The calculations demonstrate that the molecule possessing twin donor atoms along with p-type orbitals of aromatic rings significantly influenced the formation of a protective film with a uniform distribution. This configuration exhibited superior corrosion performance compared to a molecule containing only one hydroxyl group (—OH). Theoretical calculations further suggest that ACP exhibits stronger adsorption ability, with the two oxygen atoms identified as the most reactive sites, indicating their tendency to donate electrons to the iron surface. The robust resonance-stabilized structure of ACP enhances the stability of the molecules, facilitating its interaction and adsorption onto the metal surface compared to the DCP inhibitor. Overall, the superior inhibition performance of ACP is attributed to the close bonding of twin donor fragments within the ACP molecule, forming a more compact adsorbed inhibitor film on the steel surface.

Modeling the electroluminescence of atomic wires from quantum dynamics simulations.

Static and time-dependent quantum-mechanical approaches have been employed in the literature to characterize the physics of light-emitting molecules and nanostructures. However, the electromagnetic emission induced by an input current has remained beyond the realm of molecular simulations. This is the challenge addressed here with the help of an equation of motion for the density matrix coupled to a photon bath based on a Redfield formulation. This equation is evolved within the framework of the driven-Liouville von Neumann approach, which incorporates open boundaries by introducing an applied bias and a circulating current. The dissipated electromagnetic power can be computed in this context from the time derivative of the energy. This scheme is applied in combination with a self-consistent tight-binding Hamiltonian to investigate the effects of bias and molecular size on the electroluminescence of metallic and semiconducting chains. For the latter, a complex interplay between bias and molecular length is observed: there is an optimal number of atoms that maximizes the emitted power at high voltages but not at low ones. This unanticipated behavior can be understood in terms of the band bending produced along the semiconducting chain, a phenomenon that is captured by the self-consistency of the method. A simple analytical model is proposed that explains the main features revealed by the simulations. The methodology, applied here at a self-consistent tight-binding level but extendable to more sophisticated Hamiltonians such as density functional tight binding and time dependent density functional theory, promises to be helpful for quantifying the power and quantum efficiency of nanoscale electroluminescent devices.

Phonon-induced band gap renormalization by dielectric dependent global hybrid density functional tight binding

Phonon-induced band gap renormalization by dielectric dependent global hybrid density functional tight binding

Prediction of electronic density of states in guanine-TiO2 adsorption model based on machine learning

The electronic density of states is a property of the material that is extensively used in quantum systems in condensed matter physics. It refers to the energy level of the electrons in a solid crystal. One of the most current ways to compute it is by Density Functional Tight Binding (DFTB), given the geometry of the material. Nevertheless, this computation could be very computationally demanding, although applied to some materials with a very reduced number of atoms. This paper presents a method to deduce the electronic density of states, which is based on a neural network, thus, almost linear with respect to the number of atoms in the material. Specifically, we have applied our method to a metal oxide structure interacting with a nucleic base guanine. We have focused on stoichiometric and O-defective anatase TiO2 (101) surfaces. The data set of the electronic density of states needed to train the neural network has been obtained by a DFTB+ numerical solver given an initial molecular geometry model, which computed a track of time-dependent geometry and their associated electronic density of states. We validated that the predicted electronic density of states deduced by our method and by DFTB tends to be very similar, thus, opening the door to other computations such that introducing our method in the process of generating the time-dependent geometry analysis.

Probing the range of applicability of structure- and energy-adjusted QM/MM link bonds III: QM/MM MD simulations of solid-state systems at the example of layered carbon structures.

The previously introduced workflow to achieve an energetically and structurally optimized description of frontier bonds in quantum mechanical/molecular mechanics (QM/MM)-type applications was extended into the regime of computational material sciences at the example of a layered carbon model systems. Optimized QM/MM link bond parameters at HSEsol/6-311G(d,p) and self-consistent density functional tight binding (SCC-DFTB) were derived for graphitic systems, enabling detailed investigation of specific structure motifs occurring in graphene-derived structures quantum-chemical calculations. Exemplary molecular dynamics (MD) simulations in the isochoric-isothermic (NVT) ensemble were carried out to study the intercalation of lithium and the properties of the Stone-Thrower-Wales defect. The diffusivity of lithium as well as hydrogen and proton adsorption on a defective graphene surface served as additional example. The results of the QM/MM MD simulations provide detailed insight into the applicability of the employed link-bond strategy when studying intercalation and adsorption properties of graphitic materials.

Exploring the Mechanisms behind Non-aromatic Fluorescence with the Density Functional Tight Binding Method.

Recent experimental findings reveal nonconventional fluorescence emission in biological systems devoid of conjugated bonds or aromatic compounds, termed non-aromatic fluorescence (NAF). This phenomenon is exclusive to aggregated or solid states and remains absent in monomeric solutions. Previous studies focused on small model systems in vacuum show that the carbonyl stretching mode along with strong interaction of short hydrogen bonds (SHBs) remains the primary vibrational mode explaining NAF in these systems. In order to simulate larger model systems taking into account the effects of the surrounding environment, in this work we propose using the density functional tight-binding (DFTB) method in combination with non-adiabatic molecular dynamics (NAMD) and the mixed quantum/molecular mechanics (QM/MM) approach. We investigate the mechanism behind NAF in the crystal structure of l-pyroglutamine-ammonium, comparing it with the related nonfluorescent amino acid l-glutamine. Our results extend our previous findings to more realistic systems, demonstrating the efficiency and robustness of the proposed DFTB method in the context of NAMD in biological systems. Furthermore, due to its inherent low computational cost, this method allows for a better sampling of the nonradiative events at the conical intersection which is crucial for a complete understanding of this phenomenon. Beyond contributing to the ongoing exploration of NAF, this work paves the way for future application of this method in more complex biological systems such as amyloid aggregates, biomaterials, and non-aromatic proteins.

Benchmark Investigation of SCC-DFTB against Standard and Hybrid DFT to Model Electronic Properties in Two-Dimensional MOFs for Thermoelectric Applications.

Recent studies have shown that metal-organic frameworks (MOFs) have potential as thermoelectric materials, and the topic has received increasing attention. The main motivation for this project is to further our knowledge of thermoelectric properties in MOFs and find which available self-consistent-charge density functional tight binding (SCC-DFTB) method can best predict (at least trends in) the electronic properties of MOFs at a lower computational cost than standard density functional theory (DFT). In this work, the electronic properties of monolayer, serrated, AA-stacked, and/or AB-stacked Zn3C6O6, Cd3C6O6, Zn-NH-MOF─for which no previous calculations of thermoelectric performance exist─and Ni3(HITP)2 MOFs are modeled with DFT-PBE, DFT-HSE06, GFN1-xTB, GFN2-xTB, and DFTB-3ob/mio. The band structures, density of states, and their relative orbital contributions, as well as the electrical conductivity, Seebeck coefficient, and power factor, are compared across methods and geometries. Our results suggest that GFN-xTB is adequate to predict the MOFs' band structure shape and density of states but not band gap. Our calculations further indicate that Zn3C6O6, Cd3C6O6, and Zn-NH-MOF have higher power factor values than Ni3(HITP)2, one of the highest performing synthesized MOFs, and are therefore promising for thermoelectric applications.

Relation of alkyl chain length and corrosion inhibition efficiency of N-Acylated Chitosans over mild steel in acidic medium

The present investigation is focussed on unveiling the potential of ecologically safe Chitosan derived N-Acylated products (N-ACs) as anti-corrosive agents for mild steel in 0.5 M H2SO4. Electrochemical Impedance Spectroscopy (EIS) including potentiodynamic polarization (PDP) and potentiostatic electrochemical impedance were deployed to evaluate their anti-corrosive performance and the results of the same reported N-acylated chitosan derivatives project remarkable inhibition efficiency with the most effective performance of 96.80% at 250 mgL-1. The pronounced inhibition efficiency reported was conclusively accredited to the availability of heteroatoms in modified chitosan which further aid in the formation of a protective barrier over mild steel counterparts. The final acylated products were reported to be mixed type as demarcated by PDP results. Further, the prevention ability of modified chitosan was obtained via adsorption and the Langmuir adsorption model was best seen as suitable in this regard. Surface studies and theoretical modeling including Global Reactivity results, molecular Dynamic Modelling (MD), and Density Functional Bond Tight Binding Results (DFBT) were incorporated to gain insights into the molecular level of interactions between metal and N-ACs.

Enhanced Photocatalytic Activity of Surface‐Modified TiO2 with Bimetallic AuPd Nanoalloys for Hydrogen Generation

Herein, commercial titania (TiO2‐P25) is modified with mono‐ and bi‐metallic (Au, Pd, and AuPd) nanoparticles synthesized by chemical reduction method using NaBH4 as a strong reducing agent at room temperature. Bimetallic AuPd nanoalloys homogeneous in size and well dispersed on the TiO2 surface are obtained. The charge‐carrier dynamics, which is a key factor in photocatalysis, is studied by time‐resolved microwave conductivity. The results reveal that surface modification plays a crucial role in charge‐carrier separation, increasing the activity under UV–vis light irradiation. The bimetallic AuPd nanoalloys formation is confirmed by high‐angle annular dark field scanning transmission electron microscopy and corroborated by semiempirical molecular dynamics simulations (Gupta‐LAMMPS). The surface‐modified TiO2 with bimetallic AuPd nanoalloys exhibits higher photocatalytic activity compared to TiO2 modified with their monometallic counterparts. The experimental results are also supported by density functional theory and density functional tight binding calculations, which show that alloying AuPd with low Pd content presents significant synergetic effects for hydrogen generation under UV–vis light from aqueous triethanolamine solutions. Additionally, the AuPd/TiO2 photocatalysts are stable with cycling.

A multi-scale computational investigation of cationic dye interaction with rutile TiO2: An interconnected simulation approach

Metal oxides have increasingly been employed for water pollution remediation. In the present investigation, the adsorptive characteristics of three cationic dyes, rhodamine B (RB), safranin O (SO), and methylene blue (MB), on the (110) surface of rutile titanium dioxide in an aqueous milieu were scrutinized. Utilizing Density Functional Theory (DFT), the reactivity of these organic compounds was evaluated by computing parameters such as molecular frontier orbital energies, energy gap (ΔEgap), electronegativity (χ), chemical hardness (η), chemical softness (σ), electrophilicity (ω), nucleophilicity, electron transfer fraction (ΔN), and Fukui indices. Findings demonstrated that RB exhibited superior reactivity and adsorptive capacity in comparison to MB and SO. Self-Consistent Charge Density-Functional Tight-Binding (SCC-DFTB) simulations revealed that RB engaged in the most potent interactions with the TiO2(110) surface, with interaction energies following the sequence RB (−1.12 eV) > MB (−1.07 eV) > SO (−1.01 eV). Furthermore, the most energetically favorable adsorption configurations for these dyes were elucidated through Molecular Dynamics (MD) simulations. Adsorption energies calculated via MD simulations corroborated that the TiO2 (110) surface possessed high adsorptive capabilities for these cationic dyes, particularly a stronger affinity for RB. The study provides comprehensive insights into the molecular-level interactions between cationic dyes and rutile TiO2, thereby contributing valuable information for the optimization of metal oxide-based water pollution treatment technologies.

The Density Functional Tight Binding (DFTB) Approach for Investigating Vacancy and Doping in Graphene as Hydrogen Storage

A study on graphene defects for hydrogen storage has been successfully conducted using the Density Functional Tight Binding (DFTB) approach. The research aimed to modify solid materials for hydrogen storage. A 4 × 4 × 1 unit cell was used as the basis, while the supercell used for sampling was enlarged to 40 × 40 × 1. The analyzed data included changes in Density of States (DOS), Fermi level shifts, electronic band structures, formation energy, adsorption energy, and isosurfaces for each graphene orientation. It has been observed that modifying the surface structure of graphene can alter the electronic properties of graphene. This is indicated by shifts in DOS intensity, characterized by increased electronic intensity around the Fermi level total density charge different. The interaction energy between graphene and hydrogen gas has been determined to be -0.0155 eV for H-epoxy graphene, -0.4941 eV for H-monovacancy graphene, and -0.0424 eV for HN-monovacancy graphene. The presence of the vacancy increase the potential to adsorp hydrogen.

First-Principles Models of Polymorphism of Pharmaceuticals: Maximizing the Accuracy-to-Cost Ratio.

Accuracy and sophistication of in silico models of structure, internal dynamics, and cohesion of molecular materials at finite temperatures increase over time. Applicability limits of ab initio polymorph ranking that would be feasible at reasonable costs currently represent crystals of moderately sized molecules (less than 20 nonhydrogen atoms) and simple unit cells (containing rather only one symmetry-irreducible molecule). Extending the applicability range of the underlying first-principles methods to larger systems with a real-life significance, and enabling to perform such computations in a high-throughput regime represent additional challenges to be tackled in computational chemistry. This work presents a novel composite method that combines the computational efficiency of density-functional tight-binding (DFTB) methods with the accuracy of density-functional theory (DFT). Being rooted in the quasi-harmonic approximation, it uses a cheap method to perform all of the costly scans of how static and dynamic characteristics of the crystal vary with respect to its volume. Such data are subsequently corrected to agree with a higher-level model, which must be evaluated only at a single volume of the crystal. It thus enables predictions of structural, cohesive, and thermodynamic properties of complex molecular materials, such as pharmaceuticals or organic semiconductors, at a fraction of the original computational cost. As the composite model retains the solid physical background, it suffers from a minimum accuracy deterioration compared to the full treatment with the costly approach. The novel methodology is demonstrated to provide consistent results for the structural and thermodynamic properties of real-life molecular crystals and their polymorph ranking.

Exploring Molecular Energy Landscapes by Coupling the DFTB Potential with a Tree-Based Stochastic Algorithm: Investigation of the Conformational Diversity of Phthalates.

Exploring the global energy landscape of relatively large molecules at the quantum level is a challenging problem. In this work, we report the coupling of a nonredundant conformational space exploration method, namely, the robotics-inspired iterative global exploration and local optimization (IGLOO) algorithm, with the quantum-chemical density functional tight binding (DFTB) potential. The application of this fast and efficient computational approach to three close-sized molecules of the phthalate family (DBP, BBP, and DEHP) showed that they present different conformational landscapes. These differences have been rationalized by making use of descriptors based on distances and dihedral angles. Coulomb interactions, steric hindrance, and dispersive interactions have been found to drive the geometric properties. A strong correlation has been evidenced between the two dihedral angles describing the side-chain orientation of the phthalate molecules. Our approach identifies low-energy minima without prior knowledge of the potential energy surface, paving the way for future investigations into transition paths and states.

Investigating Si (100) surface patterns as well as electrical states through integrating molecular dynamics simulations with density functional tight binding at atomic level

The present work performs molecular dynamics simulations within model potential’s framework to investigate surface atoms’ rearrangements, loading states, and stress distribution in Si (100) surface on heating, and then single point energy calculations for the obtained atomic packing structures of Si substrates are used to obtain electrical information through self-consistent charge density functional tight binding approach. From analysis of Lode-Nadai parameters, atomic stress, visually packing images, Mulliken charges of atoms, Mulliken electron populations on atomic orbitals, differential charge densities, and chemical potentials for substrates, the knowledge of surface patterns, micro-loading states on the atoms and electrical states are obtained. The simulation results show the positions of vacancies have a significant impact on the rearrangements of the surface atoms. The appropriate selection of vacancy positions could be helpful in designing patterns of Si (100) surface. The vacancies in the subsurface or deeper atomic layers present migrations, where the migrations are more likely to occur at higher temperatures. There are stress concentration area and apparent charge transfers for the atoms forming dimers and neighboring atoms around the vacancies, and the differential charge densities in the surface atoms also present significant differences. These substrates with different surface patterns have different chemical potentials.

A computational study of rectification behavior of doped α-graphyne nanotubes

α-graphyne is a carbon nanosheet with sp and sp2 hybridization. This sheet is a hexagonal semi-metal like graphene. In this paper, we roll α-graphyne sheet and build zigzag nanotubes with three different size (n = 5, 6, 7). We used density functional tight binding (DFTB) to compute the band structures of nanotubes. The results show that the gap energies for n = 5, 6, 7 are about 0.648, 0.115, 0.309 eV, respectively. We add impurity atoms such as boron (B) and nitrogen (N) to nanotube. We found that the band gap energy of these doped nanotubes decrease. In addition, the structures that carbon atoms with sp2 hybridization replaced by impurity atoms are more stable. For the first time, we build two doped nanotube diodes at low (M1 model) and high (M2 model) concentration and compute current-voltage (I–V) curves with non-equilibrium Green's functions (NEGF). The I–V curves of doped nanotubes show non-linear asymmetric behavior. Form I–V curves, we compute rectification ratio and negative differential resistance (NDR). The low doped zigzag nanotube with n = 5 (M1 model) shows the highest rectification ratio (about 12.236 × 104) at 0.3 V. In addition, the rectification ratio of this diode at high concentration (M2) is about 3.67 × 103 at 0.6 V. The I–V curve of M2 model illustrations a notably negative differential resistance (NDR) at reverse bias. The ratio of current at peak to current at valley is about 26.83. To describe the I–V curves, we used transmission spectrum, the band structures of electrodes and the density of states (DOS) of central scattering region. In general, transition occurs when the energy levels of three regions (left electrode, scattering and right electrode) overlap.

Corrosion Inhibition of Expired Cefazolin Drug on Copper Metal in Dilute Hydrochloric Acid Solution: Practical and Theoretical Approaches.

In this work, we studied the corrosion of Cu metal in 0.5 mol L-1 HCl and the inhibition effect of the expired Cefazolin drug. The inhibition efficiency (IE) of Cefazolin varied according to its concentration in solution. As the Cefazolin concentration increased to 300 ppm, the IE increased to 87% at 298 K and decreased to 78% as the temperature increased to 318 K. The expired drug functioned as a mixed-type inhibitor. The adsorption of the drug on the copper surface followed Temkin's adsorption model. The magnitudes of the standard free energy change (ΔGoads) and adsorption equilibrium constant (Kads) indicated the spontaneous nature and exothermicity of the adsorption process. Energy dispersive X-ray (EDX) and scanning electron microscopy (SEM) techniques showed that the drug molecules were strongly attached to the Cu surface. The electrochemical frequency modulation (EFM), potentiodynamic polarization (PP), and electrochemical impedance spectroscopy (EIS) results were in good agreement with the results of the weight loss (WL) method. The density functional tight-binding (DFTB) and Monte Carlo (MC) simulation results indicated that the expired drug bound to the copper surface through the lone pair of electrons of the heteroatoms as well as the π-electrons of the tetrazole ring. The adsorption energy between the drug and copper metal was -459.38 kJ mol-1.

A density functional tight-binding based strategy for modeling ion bombardment and its application to Ar bombardment of silicon nitride

In many modern applications, it is important to understand mechanisms of non-equilibrium chemistry and physics that are driven by low energy ion bombardment of solid surfaces. However, the study of these processes has been challenging as it demands a relatively unique balance between chemical fidelity and computational cost. To this end, we have proposed and constructed a new, high-throughput simulation pipeline based on density functional tight binding simulations. Additionally, we have extended the parameter set pbc-0-3 with the addition of Ar, thereby enabling the simulation of Ar bombardment. This pipeline was then applied to study the structural and compositional evolution of silicon nitride (SiN) under Ar bombardment. We identified a possible rate limiting step of bombardment-driven sputtering of SiN and suggested underlying mechanisms of Si and N removal. Damage from the bombardment, including generation of surface defects and Ar implantation, are further discussed. These findings and the newly developed simulation framework will serve as a useful foundation for further research in processes driven by ion bombardment.