Density Functional Tight Binding Research Articles

Neutral-to-ionic photoinduced phase transition of tetrathiafulvalene-p-chloranil by electronic and vibrational excitation: A real-time nuclear-electronic dynamics simulation study.

Tetrathiafulvalene-p-chloranil exhibits photoinduced phase transition (PIPT) between neutral (N) and ionic (I) phases, in which the constituent molecules are approximately charge-neutral and ionic, respectively. In addition to visible-light irradiation, which can induce both N → I and I → N PIPTs, infrared irradiation has been reported to induce the N → I PIPT. These results suggest that N → I and I → N PIPTs can be driven by electronic excitation, and the I → N PIPT can also be driven by vibrational excitation. However, the feasibility of the N → I PIPT using vibrational excitation remains an open question. In this study, we address this issue by simulating the PIPT processes using a nonadiabatic molecular dynamics approach combined with real-time electron dynamics at the level of a semiempirical quantum chemical model, density-functional tight binding. The results show the importance of vibronic interactions in the PIPT processes, thereby suggesting the possibility of N → I PIPT by vibrational excitations with infrared irradiation.

UV irradiation and infrared observation of sulfur dioxide clusters and solids at cryogenic temperature

Clusters in the Ar matrix and solids of sulfur dioxide (SO2) produced at cryogenic temperature have been subject to UV irradiation, followed by spectroscopic observation in the mid-infrared region. We found that UV irradiation in the B-band (266–300 nm) of SO2 resulted in annealing and production of sulfur trioxide (SO3) even below the dissociation limit of SO2. Other sulfur oxides such as SO and S2O2 have not been detected. Observed infrared spectra have been successfully compared with theoretical simulations for global minima of (SO2)n (n = 2–10) obtained by the minima hopping method combined with density functional tight-binding (DFTB) method.

Multi-Theory Comparisons of Molecular Simulation Approaches to TiO2/H2O Interfacial Systems

Herein, we present molecular dynamics analyses of systems containing TiO2 interfaces with water, simulated using empirical forcefields (FF), Density-Functional Tight-Binding (DFTB), and Density-Functional Theory (DFT) methodologies. The results and observed differences between the methodologies are discussed, with the aim of assessing the suitability of each methodology for performing molecular dynamics simulations of catalytic systems. Generally, well-parameterised forcefield MD outperforms the other methodologies—albeit, at the expense of neglecting certain qualitative behaviours entirely. DFTB represents an attractive compromise method, and has the potential to revolutionise the field of molecular dynamics in the near future due to advances in generating parameters.

Hybrid Density Functional Tight Binding (DFTB)─Molecular Mechanics Approach for a Low-Cost Expansion of DFTB Applicability.

The density functional-based tight binding (DFTB) method has seen a rise in adoption for materials modeling, as it offers significant improvement in scalability with accuracy comparable to the density functional theory (DFT) when good parameterizations exist. The cost reduction in DFTB compared to DFT is achieved by the pre-parameterization of the elements of the Hamiltonian matrix as well as the repulsion potential between all pairs of atoms. Parameterization for new systems with accuracies competitive with DFT in specific applications requires specialized manpower and computational resources. This prevents the application of the DFTB method to systems for which it was not parameterized. In this work, we explore an approach to address the problem of missing parameters of DFTB by modeling the interactions with missing Slater-Koster parameters with an interatomic interaction potential. When the distance between two atoms modeled at the force-field level is sufficiently large, the approach results in accurate structural and electronic properties. The resulting calculation is therefore a hybrid between DFTB and molecular mechanics, a pure DFTB for atoms with a complete set of interatomic parameterizations, and a mix between DFTB and molecular mechanics for atoms with a missing interatomic parameterization. The approach is expected to be particularly useful for hybrid materials and interfaces. The method is tested on the examples of 2D materials, mixed oxides, and a large-scale calculation of an oxide-oxide interface.

Machine Learning Enhanced DFTB Method for Periodic Systems: Learning from Electronic Density of States.

Density functional tight binding (DFTB) is an approximate density functional based quantum chemical simulation method with low computational cost. In order to increase its accuracy, we have introduced a machine learning algorithm to optimize several parameters of the DFTB method, concentrating on solids with defects. The backpropagation algorithm was used to reduce the error between DFTB and DFT results with respect to the training data set and to obtain adjusted DFTB Hamiltonian and overlap matrix elements. Afterward, the generalization capability of the trained model was tested for geometries not being part of the training set. In the current work, we have focused on defective periodic silicon and silicon carbide systems as target materials and the density of states (DOS) as target property to demonstrate the feasibility of our approach. The trained model was able to reduce the differences between the DFTB and DFT DOS significantly, while other derived properties (for example, Mulliken population distribution, projected DOS) remained physically sound. Also, the transferability of the obtained model could be verified. Our method allows to carry out relatively fast simulations with high accuracy and only moderate training efforts, and represents a good compromise for cases, where long-range effects make direct machine learning predictions difficult.

Beyond the Status Quo: Density Functional Tight Binding and Neural Network Potentials as a Versatile Simulation Strategy to Characterize Host-Guest Interactions in Metal- and Covalent Organic Frameworks.

In recent years, research focused on synthesis, characterization, and application of metal-organic frameworks (MOFs) has attracted increased interest, from both an experimental as well as a theoretical perspective. Self-consistent charge density functional tight binding (SCC DFTB) in conjunction with a suitable constrained molecular dynamics (MD) simulation protocol provides a versatile and flexible platform for the study of pristine MOFs as well as guest@MOF systems. Although being a semi-empirical quantum mechanical method, SCC DFTB inherently accounts for polarization and many-body contributions, which may become a limiting factor in purely force field-based simulation studies. A number of examples such as CO2, indigo, and drug molecules embedded in various MOF hosts are discussed to highlight the capabilities of the presented simulation approach. Furthermore, a promising extension of the outlined simulation strategy toward the treatment of covalent organic frameworks utilizing state-of-the-art neural network potentials providing a description at DFT accuracy and force field cost is outlined.

Black box vs gray box: Comparing GAP and GPrep-DFTB for ruthenium and ruthenium oxide.

The increasing popularity of machine learning (ML) approaches in computational modeling, most prominently ML interatomic potentials, opened possibilities that were unthinkable only a few years ago-structure and dynamics for systems up to many thousands of atoms at an abinitio level of accuracy. Strictly referring to ML interatomic potentials, however, a number of modeling applications are out of reach, specifically those that require explicit electronic structure. Hybrid ("gray box") models based on, e.g., approximate, semi-empirical abinitio electronic structure with the aid of some ML components offer a convenient synthesis that allows us to treat all aspects of a certain physical system on the same footing without targeting a separate ML model for each property. Here, we compare one of these [Density Functional Tight Binding with a Gaussian Process Regression repulsive potential (GPrep-DFTB)] with its fully "black box" counterpart, the Gaussian approximation potential, by evaluating performance in terms of accuracy, extrapolation power, and data efficiency for the metallic Ru and oxide RuO2 systems, given exactly the same training set. The accuracy with respect to the training set or similar chemical motifs turns out to be comparable. GPrep-DFTB is, however, slightly more data efficient. The robustness of GPRep-DFTB in terms of extrapolation power is much less clear-cut for the binary system than for the pristine system, most likely due to imperfections in the electronic parametrization.

Analytical excited state gradients for time-dependent density functional theory plus tight binding (TDDFT + TB).

Understanding photoluminescent mechanisms has become essential for photocatalytic, biological, and electronic applications. Unfortunately, analyzing excited state potential energy surfaces (PESs) in large systems is computationally expensive, and hence limited with electronic structure methods such as time-dependent density functional theory (TDDFT). Inspired by the sTDDFT and sTDA methods, time-dependent density functional theory plus tight binding (TDDFT + TB) has been shown to reproduce linear response TDDFT results much faster than TDDFT, particularly in large nanoparticles. For photochemical processes, however, methods must go beyond the calculation of excitation energies. Herein, this work outlines an analytical approach to obtain the derivative of the vertical excitation energy in TDDFT + TB for more efficient excited state PES exploration. The gradient derivation is based on the Z vector method, which utilizes an auxiliary Lagrangian to characterize the excitation energy. The gradient is obtained when the derivatives of the Fock matrix, the coupling matrix, and the overlap matrix are all plugged into the auxiliary Lagrangian, and the Lagrange multipliers are solved. This article outlines the derivation of the analytical gradient, discusses the implementation in Amsterdam Modeling Suite, and provides proof of concept by analyzing the emission energy and optimized excited state geometry calculated by TDDFT and TDDFT + TB for small organic molecules and noble metal nanoclusters.

Theoretical analysis of thermal spikes during ion bombardment of amorphous silicon nitride surfaces

Ion bombardment of amorphous silicon nitride (a-SiN) was simulated with self-consistent-charge density functional tight binding. These simulations were used to study bombardment-induced local heating events (often called “thermal spikes”). A model for estimating the thermal conductivity (k) of a locally heated area was implemented, yielding a predicted k of 3 W/m K for a small region of the a-SiN substrate around an ion impact site. Based on the estimated k, a bombardment-induced thermal spike is predicted to return to the baseline substrate temperature after only 0.2 ps. Consequently, no cumulative heating would occur in substrates with similar k values, given a typical ion flux of less than 1 × 1018 s−1 cm−2. Our simulations also show that surface reactions toward molecular adsorbates are not significantly facilitated by the thermal spike, largely due to its short duration.

Effect of vacancy defect and strain on the structural, electronic and magnetic properties of carbon nitride 2D monolayers by DFTB method

We investigate the electronic and magnetic properties of CN (C6N6, C2N, C3N and C3N4) using density functional tight-binding (DFTB) method. We find that these compounds are dynamically stable and their electronic band gaps are in the range of 0.59–3.28 eV. We show that the electronic structure is modulated by strain and the semiconducting behavior is well preserved except for C3N at +5% biaxial strain, where a transition from semiconductor to metal was observed. Under +3% biaxial strain, C3N4 undergoes a transition from an indirect (K-Γ) to a direct (Γ-Γ) band gap. Moreover, band gap of C2N transforms from direct (Γ-Γ) to indirect (M-Γ) under +4% biaxial strain. However, no change in the nature of the band gap of C6N6. Further, when the studied materials under uniaxial tensile strain, their bandgaps reduce. Our theoretical study showed that an indirect-to-direct nature transition may occur for C6N6 and for C3N4, which broadens their applications. On the other hand, magnetism is observed in all N-vacancy defected CN, which encourages its application in spintronic. Moreover, calculations of formation energies indicate that N-vacancy is energetically more favorable than C-vacancy in both C2N and C3N4. Opposite behavior found for C6N6 and C3N.

Experimental assessment and molecular-level exploration of the mechanism of action of Nettle (Urtica dioica L.) plant extract as an eco-friendly corrosion inhibitor for X38 mild steel in sulfuric acidic medium

The development of green corrosion inhibitors has gained considerable importance in recent years due to their minimal environmental impact and sustainable nature. As industries increasingly seek eco-friendly alternatives to conventional corrosion inhibitors, research focused on identifying effective and renewable corrosion protection solutions becomes imperative. The main purpose of this study is to evaluate the inhibition action of Nettle extract (NE) (Urtica dioica L.) as a promising green corrosion inhibitor for mild steel in a 0.5 mol/L H2SO4 medium. In this work, the corrosion protection performance of NE was assessed using weight loss, electrochemical, surface characterization, and computational chemistry methods. The results indicated that NE acted as an effective corrosion inhibitor, with inhibition efficiency reaching a maximum of 90% at a concentration of 4 g L-1. Polarization studies revealed that NE functions as a mixed-type inhibitor, while electrochemical impedance spectroscopy (EIS) results showed an increase in charge transfer resistance and a decrease in double layer capacitance values in the presence of NE. Surface characterization analysis confirmed the formation of a protective NE layer on the steel surface. Furthermore, Density-functional tight-binding (DFTB) simulations identified Quercetin, Kaempferol, and Serotonin as having stronger chemical bonding with the Fe(110) surface, while Histamine molecules exhibited physical interactions with iron atoms. This comprehensive evaluation of NE's inhibition action not only supports its potential as an eco-friendly inhibitor for mild steel corrosion protection but also contributes to the development of sustainable corrosion control strategies.

DFTBephy: A DFTB-based approach for electron–phonon coupling calculations

The calculation of the electron–phonon coupling from first principles is computationally very challenging and remains mostly out of reach for systems with a large number of atoms. Semi-empirical methods, like density functional tight binding (DFTB), provide a framework for obtaining quantitative results at moderate computational costs. Herein, we present a new method based on the DFTB approach for computing electron–phonon couplings and relaxation times. It interfaces with phonopy for vibrational modes and dftb+ to calculate transport properties. We derive the electron–phonon coupling within a non-orthogonal tight-binding framework and apply them to graphene as a test case.

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials.

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

Efficient predictions of formation energies and convex hulls from density functional tight binding calculations

• DFTB was interfaced with CASM to enable efficient calculations of formation energies • Efficiency and accuracy of DFTB vs. DFT was carried out for SiC and ZnO materials • DFTB can be an order of magnitude faster than DFT for predicting formation energies Defects in materials significantly alter their electronic and structural properties, which affect the performance of electronic devices, structural alloys, and functional materials. However, calculating all the possible defects in complex materials with conventional Density Functional Theory (DFT) can be computationally prohibitive. To enhance the efficiency of these calculations, we interfaced Density Functional Tight Binding (DFTB) with the Clusters Approach to Statistical Mechanics (CASM) software package for the first time to calculate formation energies and convex hulls. Using SiC and ZnO as representative examples, we show that DFTB gives accurate results and can be used as an efficient computational approach for calculating and pre-screening formation energies/convex hulls. Our DFTB+CASM implementation allows for an efficient exploration (up to an order of magnitude faster than DFT) of formation energies and convex hulls, which researchers can use to probe other complex systems.

Mechanism of proton-coupled electron transfer described with QM/MM implementation of coupled-perturbed density-functional tight-binding.

Coupled-perturbed equations for degenerate orbitals were implemented for third order density-functional tight binding, which allowed the use of Mulliken charges as reaction coordinates. The method was applied to proton-coupled electron transfer (PCET) reactions in a model system and thoroughly tested for QM and QM/MM setups (i.e., coupled quantum and molecular mechanics). The performed enhanced sampling simulations were stable, and the obtained potentials of the mean force were able to address the thermodynamic and kinetic features of the reactions by showing the expected topography and energy barriers. Hence, this method has the potential to distinguish between concerted and sequential mechanisms and could next be applied to proton-coupled electron transfer reactions in more complex systems like proteins.

Density Functional Tight-Binding Model for Lithium-Silicon Alloys.

The predictive power of molecular dynamic simulations is mainly restricted by the time scale and model accuracy. Many systems of current relevance are of such complexity that they require addressing both issues simultaneously. This is the case of silicon electrodes in Li-ion batteries, where different LixSi alloys are formed during charge/discharge cycles. While first-principles treatments for this system are seriously limited by the computational cost of exploring its large conformational space, classical force fields are not transferable enough to represent it accurately. Density Functional Tight Binding (DFTB) is an intermediate complexity approach capable of capturing the electronic nature of different environments with a relatively low computational cost. In this work, we present a new set of DFTB parameters suited to model amorphous LixSi alloys. LixSi is the usual finding upon cycling the Si electrodes in the presence of Li ions. The model parameters are constructed with a particular emphasis on their transferability for the entire LixSi composition range. This is achieved by introducing a new optimization procedure that weights stoichiometries differently to improve the prediction of their formation energies. The resulting model is shown to be robust for predicting crystal and amorphous structures for the different compositions, giving excellent agreement with DFT calculations and outperforming state-of-the-art ReaxFF potentials.

Accelerating the density-functional tight-binding method using graphical processing units.

Acceleration of the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) was accomplished using the MAGMA linear algebra library. Two major computational bottlenecks of DFTB ground-state calculations were addressed in our implementation: the Hamiltonian matrix diagonalization and the density matrix construction. The code was implemented and benchmarked on two different computer systems: (1) the SUMMIT IBM Power9 supercomputer at the Oak Ridge National Laboratory Leadership Computing Facility with 1-6 NVIDIA Volta V100 GPUs per computer node and (2) an in-house Intel Xeon computer with 1-2 NVIDIA Tesla P100 GPUs. The performance and parallel scalability were measured for three molecular models of 1-, 2-, and 3-dimensional chemical systems, represented by carbon nanotubes, covalent organic frameworks, and water clusters.

Self-Consistent-Charge Density-Functional Tight-Binding Parameters for Modeling an All-Solid-State Lithium Battery.

All-solid-state lithium-ion batteries have been a promising solution for next-generation energy storage due to their safety and potentially high energy density. In this work, we developed a density-functional tight-binding (DFTB) parameter set for modeling solid-state lithium batteries, focusing on the band alignment at electrolyte/electrode interfaces. Despite DFTB being widely applied in the simulation of large-scale systems, parametrization is usually done for single materials, and less attention is paid to band alignment among multiple materials. Band offsets at the electrolyte/electrode interfaces are key quantities determining the performance. Here, an automated global optimization method based on DFTB confinement potentials of all elements is developed, while the band offsets between electrodes and electrolytes are introduced as constraints during the optimization. The parameter set is applied to model an all-solid-state Li/Li2PO2N/LiCoO2 battery, and its electronic structure shows a good agreement with that from density-functional theory (DFT) calculations.

Metallic water: Transient state under ultrafast electronic excitation.

The modern means of controlled irradiation by femtosecond lasers or swift heavy ion beams can transiently produce such energy densities in samples that reach collective electronic excitation levels of the warm dense matter state, where the potential energy of interaction of the particles is comparable to their kinetic energies (temperatures of a few eV). Such massive electronic excitation severely alters the interatomic potentials, producing unusual nonequilibrium states of matter and different chemistry. We employ density functional theory and tight binding molecular dynamics formalisms to study the response of bulk water to ultrafast excitation of its electrons. After a certain threshold electronic temperature, the water becomes electronically conducting via the collapse of its bandgap. At high doses, it is accompanied by nonthermal acceleration of ions to a temperature of a few thousand Kelvins within sub-100fs timescales. We identify the interplay of this nonthermal mechanism with the electron-ion coupling, enhancing the electron-to-ions energy transfer. Various chemically active fragments are formed from the disintegrating water molecules, depending on the deposited dose.

Structures and electron states of (4,4)CNT-graphene hybrid system through DFTB investigation

Self-consistent density functional tight binding (SCC-DFTB) algorithm is performed to investigate the connection of a (4,4) carbon nanotube and graphene having topological defects. The examined seamless coupling CNT-graphene configurations are characterized by packing geometries, bond length, bonding energy, chemical potential, HOMO-LUMO energy gap, Mulliken populations on atoms, charge density differences, and molecular orbitals on HOMO and LUMO energy levels. Simulation results show that coupling models among the bottom atoms of the tube and the graphene atoms around the defect as well as defect patterns greatly affect configurations and electrical structures of coupled systems. Graphene having some defective patterns improves high possibility for strongly bonding the tubes. Apparent charge transfer occurs among the atoms in the tubes and graphene, and differences of transferred charges can be also found in the coupling modes. In addition, for the atoms at the connecting knots, their s and p orbitals present significant differences in the abilities of losing and gaining charge. Charge density differences as well as molecular orbitals on the HOMO and LUMO energy levels indicate the bonding between the tube and graphene as well as electron’s distribution in space.