Density-functional Tight-binding Method Research Articles

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.

Random Green’s Function Method for Large-Scale Electronic Structure Calculation

We report a linear-scaling random Green’s function (rGF) method for large-scale electronic structure calculation. In this method, the rGF is defined on a set of random states and is efficiently calculated by projecting onto Krylov subspace. With the rGF method, the Fermi–Dirac operator can be obtained directly, avoiding the polynomial expansion to Fermi–Dirac function. To demonstrate the applicability, we implement the rGF method with the density-functional tight-binding method. It is shown that the Krylov subspace can maintain at small size for materials with different gaps at zero temperature, including H2O and Si clusters. We find with a simple deflation technique that the rGF self-consistent calculation of H2O clusters at T = 0 K can reach an error of ∼ 1 meV per H2O molecule in total energy, compared to deterministic calculations. The rGF method provides an effective stochastic method for large-scale electronic structure simulation.

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.

Structure and electrical properties of bilayer zigzag silicene nanoribbons depending on stone-wales and applied electric fields by SCC-DFTB method

The influence of geometry and electrical properties of Stone-Wales (SW) defects and an applied electric field on bilayer zigzag silicene nanoribbons (ZSiNRs) were investigated using the self-consistent charge density functional tight-binding (SCC-DFTB) method. The results show that concave deformation occurs at SW defects, leading to varying degrees of disruption in the original symmetrical structure. The overall stability of the marginal Stone-Wales (MSW) nanoribbons surpasses that of the central Stone-Wales (CSW) nanoribbons, SW-II type defects are prone to form in bilayer nanoribbons. The band gap of MSW defective nanoribbons expands significantly, realizing the transition from metallic to semi-metallic and semi-conductor properties. The electrons are transferred from the inner to the outer side of the nanoribbons, with a higher prevalence of defect nanoribbons compared to those that are perfect. The electric field intensity significantly alters the properties of both MSW-I and MSW-II defective nanoribbons.

Which molecules can challenge density-functional tight-binding methods in evaluating the energies of conformers? investigation with machine-learning toolset

Large organic molecules and biomolecules can adopt multiple conformations, with the occurrences determined by their relative energies. Identifying the energetically most favorable conformations is crucial, especially when interpreting spectroscopic experiments conducted under cryogenic conditions. When the effects of irregular surrounding medium, such as noble gas matrices, on the vibrational properties of molecules become important, semi-empirical (SE) quantum-chemical methods are often employed for computational simulations. Although SE methods are computationally more efficient than first-principle quantum-chemical methods, they can be inaccurate in determining the energies of conformers in some molecules while displaying good accuracy in others. In this study, we employ a combination of advanced machine learning techniques, such as graph neural networks, to identify molecules with the highest errors in the relative energies of conformers computed by the semi-empirical tight-binding method GFN1-xTB. The performance of three different machine learning models is assessed by comparing their predicted errors with the actual errors in conformer energies obtained via the GFN1-xTB method. We further applied the ensemble machine-learning model to a larger collection of molecules from the ChEMBL database and identified a set of molecules as being challenging for the GFN1-xTB method. These molecules hold potential for further improvement of the GFN1-xTB method, showcasing the capability of machine learning models in identifying molecules that can challenge its physical model.

Structure and electrical properties of double-walled silicon nanotubes depending on 585 defects and applied electric fields by SCC-DFTB method

The geometric structural and electronic properties of 585 defective double-walled silicon nanotubes (DWSiNTs) by using the self-consistent charge density functional tight-binding (SCC-DFTB) method. Furthermore, we investigated the impact of external electric field and intensity on the electronic properties. After the relaxation of perfect tubes, the structure of the tube changes significantly, and the winding direction of the tube wall changes. The buckling value of the tube wall is most closely related to the geometric shape and atomic arrangement order of tubes. The overall stability of zigzag tubes is higher than armchair tubes, and (6,6)@(10,10) stability is significantly affected by defects. The partially defected zigzag tubes change from direct band gap semiconductor properties to indirect band gap semi-metal properties. A transformation of the semiconductor-semimetal properties, direct-indirect band gap of DWSiNTs is realized. The charge transfer amplitude of the defect tubes is greater than the perfect tube. The applied electric field makes the stability of all tubes decrease linearly, and the stability of the defect tubes is higher than the perfect tube. Part of the electric field strength changes the defect tubes from semiconductor properties to semi-metal properties, and semi-metal properties to metal properties. These theoretical studies provide a reference for the preparation and future application of DWSiNTs.

Theoretical studies on dynamic properties and intermolecular interactions of 2,4-dinitroimidazole crystals with different impurity defects

The density functional tight binding method and DFTB-based molecular dynamics simulations were used to study the intermolecular interactions and dynamic properties of 2,4-dinitroimidazole crystals doped with different amounts of 1,4-dinitroimidazole at different temperatures.

Prediction of induced magnetism in 2D Ti2C based MXenes by manipulating the mixed surface functionalization and metal substitution computed by xTB model Hamiltonian of the DFTB method.

We employed the recently developed density functional tight binding (DFTB) method's Hamiltonian, GFN1-xTB, for modeling the mixed termination in Ti2C MXenes, namely three types of termination by combining -O and -OH, -O and -F, and -F and -OH. We demonstrated that the approach yields reliable predictions for the electronic and magnetic properties of such MXenes. The first highlighted result is that the mixed surface functionalization in Ti2CAxBy MXenes induces spin polarization with diverse magnetic alignments, including ferromagnetism and two types of antiferromagnetism. We further identified the magnetic alignment for the investigated MXene in terms of the compositions of the terminal groups. Moreover, the effect of the transition metal (Ti) substituted by the Sc atom on the electronic and magnetic properties was also investigated. We found that the studied systems maintain the magnetism and the metallic characteristics. A magnetic transition from antiferromagnetic (AFM) to ferrimagnetic (FiM) ordering was found for ScTi15C8F8(OH)8 and ScTi15C8F12(OH)4 compounds. Finally, we proved that incorporating the Sc atom into the lattice of Ti2CO2 and the mixed surface termination in Ti2CAxBy is an effective strategy to induce magnetism. Our study may provide a new potential application for designing MXene-based spintronics.

Variation in properties of double-walled zigzag and armchair silicon nanotubes depending on SW defects and applied electric fields by SCC-DFTB method

The geometric structure and electrical properties of the zigzag DWSiNT (8,0)@(12,0) and armchair DWSiNT (5,5)@(7,7) perfect tubes with the same diameter are simulated by self-consistent charge density functional tight binding method (SCC-DFTB). We considered the defective tubes by introducing three types of Stone-Wales (SW) defects, and further investigated the impact of both strong and weak electric fields on these defective tubes. Calculations demonstrate that the atomic arrangement regularity, degree of buckling, stability, energy gap, and charge distribution strongly depend on the type of perfect tubes. Following the introduction of the SW defects, the symmetry of the tubes disappeared, and there is a obvious atomic aggregation occurring at the defects. The armchair tubes demonstrate a higher stability compared to zigzag tubes and all exhibit metallic properties. SWⅡ and SWⅢ defects in zigzag tubes result in a transition from semiconductor to semi-metallic properties. The number of charge transfer increases, and the degree of atomic aggregation is more dispersed. The effect of applied electric field strength on perfect zigzag tubes is more obvious. The weak field has minimally impacted on the stability, and the strong field makes the stability improved to some extent. The presence of a strong field caused the energy gap values of (8,0)@(12,0) tube decrease sharply, and all tubes are transformed into semi-metal or metal. The weak electric field has a more pronounced regulation of band gap within the range of 0 V/nm - 0.3 V/nm on the inner tube. The consequences of this investigation can certainly be helpful in future experimental studies.

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties

The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).

Green-synthetized β-amino-α-carbethoxy ethyl acrylates as corrosion inhibitors for mild steel in acid media: Experimental performance evaluation and atomic/molecular-level modeling

Two β-amino acrylate derivatives were synthesized through a solvent- and heating-free mechanochemical procedure. Those substances were tested as corrosion inhibitors for mild steel in acidic environment (1 mol/L HCl). Weight Loss Study demonstrated that both molecules mitigate corrosion with 85 and 90 % efficiencies. Electrochemical experiments proved that the corrosion mechanism is charge transfer and that the presence of EOAB and DBMM at any tested concentration in the electrolyte significantly increases the polarization resistance and lowers the corrosion density current. This phenomenon is due to the adsorption of the molecules on the mild steel surface, leading to the formation of a protection film that prevents the corrosion reaction, as suggested by Atomic Force Microscopy topographic images. The self-consistent-charge density-functional tight-binding method (SCC-DFTB) revealed that their adsorption strength on the iron surface followed well the corrosion inhibition performance. Both inhibitor molecules formed covalent bonds with iron atoms as evidenced from stable adsorption geometries and projected density of states. Quantum chemical parameters seem to fail in predicting the experimental performance. Outcomes from the present study confirmed the fact that an additional functional group would lead to an increased inhibition performance is not a granted correlation.

Application of Taguchi method, response surface methodology, DFT calculation and molecular dynamics simulation into the removal of orange G and crystal violet by treated biomass

In this work, the efficiency of the treated plant Carpobrotus edulis (TPCE) as an effective biosorbent for removing the orange G (OG) and crystal violet (CV) dyes from aqueous solution was investigated. TPCE was characterized by FT-IR, Ss, pHz and SEM-EDX. The influence of parameters such as bioadsorbent dose, contact time, initial concentration, temperature and pH was tested using Taguchi experimental design (TED) with L8 orthogonal array (five parameters in two levels). The initial concentration, bioadsorbent dose and contact time are the main parameters for the removal of CV and OG dyes, while the effects of pH and temperature are minimal. The maximum removal efficiency of dyes under optimal operating conditions was 97.93 % and 92.68 %, respectively. which at the optimal conditions of 3 g/L, pH 10, 20 mg/L, 35 °C, 5 min and 15 g/L, pH 4, 20 mg/L, 35 °C, 60 min for CV and OG dyes, respectively. The results of response surface methodology (RSM) and analysis of variance (ANOVA) showed that the initial concentration Ci of CV dye was the most significant factor in the adsorption efficiency with a contribution of 51.56 %. On the other hand, the OG bioadsorbent dose is the most important factor in adsorption efficiency with a percentage contribution of 56.41 %. The Density Functional Tight Binding (DFTB) method shows that dyes strongly bind the adsorbent surface. Monte Carlo and molecular dynamics simulations show significant interactions between dye and adsorbent surface. The reusability of biomaterial indicated that the adsorption performance dropped very slightly up to five cycles.

Multipole Expansion of Atomic Electron Density Fluctuation Interactions in the Density-Functional Tight-Binding Method.

The accuracy of the density-functional tight-binding (DFTB) method in describing noncovalent interactions is limited due to its reliance on monopole-based spherical charge densities. In this study, we present a multipole-extended second-order DFTB (mDFTB2) method that takes into account atomic dipole and quadrupole interactions. Furthermore, we combine the multipole expansion with the monopole-based third-order contribution, resulting in the mDFTB3 method. To assess the accuracy of mDFTB2 and mDFTB3, we evaluate their performance in describing noncovalent interactions, proton transfer barriers, and dipole moments. Our benchmark results show promising improvements even when using the existing electronic parameters optimized for the original DFTB3 model. Both mDFTB2 and mDFTB3 outperform their monopole-based counterparts, DFTB2 and DFTB3, in terms of accuracy. While mDFTB2 and mDFTB3 perform comparably for neutral and positively charged systems, mDFTB3 exhibits superior performance over mDFTB2 when dealing with negatively charged systems and proton transfers. Overall, the incorporation of the multipole expansion significantly enhances the accuracy of the DFTB method in describing noncovalent interactions and proton transfers.

Diversity of Behavior after Collisions of Sn and Si Nanoparticles Found Using a New Density Functional Tight-Binding Method.

We present a new approach to studying nanoparticle collisions using density functional based tight binding (DFTB). A novel DFTB parametrization has been developed to study the collision process of Sn and Si clusters (NPs) using molecular dynamics (MD). While bulk structures were used as training sets, we show that our model is able to accurately reproduce the cohesive energy of the nanoparticles using density functional theory (DFT) as a reference. A surprising variety of phenomena are revealed for the Si/Sn nanoparticle collisions, depending on the size and velocity of the collision: from core-shell structure formation to bounce-off phenomena.

NMR, x-ray, and Mössbauer results for amorphous Li-Si alloys using density functional tight-binding method

NMR, x-ray, and Mössbauer results for amorphous Li-Si alloys using density functional tight-binding method

A Mini-Review on Marine Bioactive Compounds as a Source of Antiviral Drug Lead

Abstract: Currently, pathogens or some microorganisms that are dangerous to human society cause plenty of diseases. All these diseases are spreading because of the limited available treatment options. Some pathogens species are very dangerous to humankind, particularly viral pathogens. These viral pathogens (viruses) have become resistant and immune to many of the medications, thus requiring more and more drug molecules or treatments to be discovered. Many new drugs or treatments are discovered and under clinical trials, and many are being tested. Presently, researchers are in continuous search of newer drugs or molecules using synthetic chemistry or exploring natural resources. However, synthesizing a drug molecule is time-consuming and requires more workforce and much more costly equipment. To tackle this challenge, the use of computational approaches provided complementary information that may also help in the discovery of new molecules. Recently, the fragment molecular orbit based density-functional tight-binding method (DFTB) of drug discovery of drug-like lead is rapid and accurate as compared to conventional computational approaches. Natural resources from land and water can become a choice as an alternative option to discover new entities. The marine environment provides us with a broad spectrum of chemically diverse antiviral compounds isolated from many marine organisms such as bacteria, fungi, sponges, algae, etc. Now, the natural marine molecule as an antiviral agent harrows a new way to modern medicine.

Talarolide A and Talaropeptides A-D: Potential Marine-Derived Therapeutic Peptides with Interesting Chemistry and Biological Activity Studied through Density Functional Theory (DFT) and Conceptual DFT.

Molecules sourced from marine environments hold immense promise for the development of novel therapeutic drugs, owing to their distinctive chemical compositions and valuable medicinal attributes. Notably, Talarolide A and Talaropeptides A-D have gained recent attention as potential candidates for pharmaceutical applications. This study aims to explore the chemical reactivity of Talarolide A and Talaropeptides A-D through the application of molecular modeling and computational chemistry techniques, specifically employing Conceptual Density Functional Theory (CDFT). By investigating their chemical behaviors, the study seeks to contribute to the understanding of the potential pharmacological uses of these marine-derived compounds. The molecular geometry optimizations and frequency calculations were conducted using the Density Functional Tight Binding (DFTBA) method. This was followed by a subsequent round of geometry optimization, frequency analysis, and computation of electronic properties and chemical reactivity descriptors. We employed the MN12SX/Def2TZVP/H2O model chemistry, utilizing the Gaussian 16 program and the SMD solvation model. The analysis of the global reactivity descriptors arising from CDFT was achieved as well as the graphical comparison of the dual descriptor DD revealing the areas of the molecules with more propensity to suffer a nucleophilic or electrophilic attack. Additionally, Molinspiration and SwissTargetPrediction were considered for the calculation of molecular characteristics and predicted biological targets. These include enzymes, nuclear receptors, kinase inhibitors, GPCR ligands, and ion channel modulators. The graphical results show that Talarolide A and the Talaropeptides A-D are likely to behave as protease inhibitors.

Semiempirical Methods for Molecular Systems in Strong Magnetic Fields.

A general scheme is presented to extend semiempirical methods to include the effects of arbitrary strength magnetic fields, while maintaining computational efficiency. The approach utilizes three main modifications; a London atomic orbital (LAO) basis set is introduced, field-dependent kinetic energy corrections are added to the model Hamiltonian, and spin-Zeeman interaction energy terms are included. The approach is applied to the widely available density-functional tight-binding method GFN1-xTB. Considering the basis set requirements for the kinetic energy corrections in a magnetic field leads to two variants: a single-basis approach GFN1-xTB-M0 and a dual-basis approach GFN1-xTB-M1. The LAO basis in the latter includes the appropriate nodal structure for an accurate representation of the kinetic energy corrections. The variants are assessed by benchmarking magnetizabilities and nuclear magnetic resonance shielding constants calculated using weak magnetic fields. Remarkably, the GFN1-xTB-M1 approach also exhibits excellent performance for strong fields, || ≤ 0.2B0 (B0 = 2.3505 × 105 T), recovering exotic features such as the para- to dia-magnetic transition in the BH molecule and the preferred electronic configuration, molecular conformation, and orientation of benzene. At stronger field strengths, || > 0.2B0, a degradation in the quality of the results is observed. The utility of GFN1-xTB-M1 is demonstrated by performing conformer searches in a range of field strengths for the cyclooctatetraene molecule, with GFN1-xTB-M1 capturing the transition from tub to planar conformations at high field, consistent with much more computationally demanding current-density functional theory calculations. Magnetically induced currents are also shown to be well described for the benzene and infinitene molecules, the latter demonstrating the flexibility and computational efficiency of the approach. The GFN1-xTB-M1 approach is a useful tool for the study of structure, conformation, and dynamics of large systems in magnetic fields at the semiempirical level as well as for preoptimization of molecular structure in ab initio calculations, enabling more efficient exploration of complex potential energy surfaces and reactivity in the presence of external fields.

Toward Quantum Chemical Free Energy Simulations of Platinum Nanoparticles on Titania Support.

Platinum nanoparticles (Pt-NPs) supported on titania surfaces are costly but indispensable heterogeneous catalysts because of their highly effective and selective catalytic properties. Therefore, it is vital to understand their physicochemical processes during catalysis to optimize their use and to further develop better catalysts. However, simulating these dynamic processes is challenging due to the need for a reliable quantum chemical method to describe chemical bond breaking and bond formation during the processes but, at the same time, fast enough to sample a large number of configurations required to compute the corresponding free energy surfaces. Density functional theory (DFT) is often used to explore Pt-NPs; nonetheless, it is usually limited to some minimum-energy reaction pathways on static potential energy surfaces because of its high computational cost. We report here a combination of the density functional tight binding (DFTB) method as a fast but reliable approximation to DFT, the steered molecular dynamics (SMD) technique, and the Jarzynski equality to construct free energy surfaces of the temperature-dependent diffusion and growth of platinum particles on a titania surface. In particular, we present the parametrization for Pt-X (X = Pt, Ti, or O) interactions in the framework of the second-order DFTB method, using a previous parametrization for titania as a basis. The optimized parameter set was used to simulate the surface diffusion of a single platinum atom (Pt1) and the growth of Pt6 from Pt5 and Pt1 on the rutile (110) surface at three different temperatures (T = 400, 600, 800 K). The free energy profile was constructed by using over a hundred SMD trajectories for each process. We found that increasing the temperature has a minimal effect on the formation free energy; nevertheless, it significantly reduces the free energy barrier of Pt atom migration on the TiO2 surface and the transition state (TS) of its deposition. In a concluding remark, the methodology opens the pathway to quantum chemical free energy simulations of Pt-NPs' temperature-dependent growth and other transformation processes on the titania support.