Density Functional Tight Binding Model Research Articles

Sensitivity enhancement of SWCNT gas sensors by nitrogen plasma treatment

Single-walled carbon nanotubes (SWCNTs) hold promise for detecting toxic and chemically aggressive gases due to their unique 1D structure. However, the limited sensitivity of as-produced SWCNT films hampers their practical application as gas sensors. To enhance sensitivity, various functionalization methods, including nitrogen doping, have been explored. In this study, we investigated the impact of low-frequency nitrogen plasma treatment on SWCNT films for sensing reducing and oxidizing air pollutants—specifically, NO2 and NH3—at concentrations ranging from 10 to 50 ppm. TEM and UV-vis-NIR unveiled the etching of the SWCNT structure, and Raman spectroscopy showed the formation of defects following nitrogen plasma treatment. XPS analysis confirmed the presence of amine, pyridinic, and quaternary N-containing defects within the graphitic structure. Upon treating pristine SWCNT films with a 32 W·h dose of plasma, the average chemiresistive response to 50 ppm analyte (in air) at room temperature increased significantly by 100x, rising from 0.2% to 20% (NO2) and from 0.05% to 17% (NH3). Density functional tight-binding modeling corroborated the experimental findings, revealing a correlation between plasma dose and sensor signal. Moreover, the modeling suggested that analyte molecule adsorption on defects is favored over basal plane adsorption.

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.

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.

Exploring the feasibility of new eco-friendly heterocyclic compounds for establishing efficient corrosion protection for N80 steel in a simulated oil well acidizing environment: From molecular-level prediction to experimental validation

Corrosion inhibitors are an effective and low-cost option to relieve strong acid-induced corrosion during acidizing oil wells. In this work, a computational study was conducted to assess the interaction of newly synthesized eco-friendly compounds namely, (R, E)-N′-(4-(dimethylamino)benzylidene)− 2-(4-isobutylphenyl) propanehydrazide (DBIP) and (R,E)− 2-(4-isobutylphenyl)-N′-(4-nitrobenzylidene) propanehydrazide (INBP) with the steel surface. Theoretical prediction of compounds’ affinity to the steel surface and interaction mechanisms were elucidated at the molecular level using first-principles density-functional tight-binding (DFTB), quantum chemical descriptors (QCDs), Fukui functions (FFs) and molecular dynamics (MD) simulations. Then, the corrosion inhibition performance of selected compounds for N80 steel in 15 wt% HCl solution was investigated using weight loss (WL), electrochemical impedance spectroscopy (EIS), potentiodynamic polarization curves (PPCs) methods, and field emission scanning electron microscopy (FESEM). A theoretical investigation by DFTB modeling and Partial Density of States (PDOSs) showed the formation of several covalent bonds between inhibitor molecules and Fe(110) surface. Experimental studies revealed an increased inhibition efficiency of both inhibitors DBIP and INBP with increasing concentrations reaching a higher efficiency of 99% at 5 × 10−3 mol/L of DBIP. The PPCs data confirmed the mixed-type nature of inhibition. The adsorption obeyed the Langmuir isotherm model, following a mixed physical and chemical interaction mode. The surface morphology analysis by FESEM showed a protected N80 steel surface in the presence of inhibitors compared to its highly corroded surface in blank solutions. The present results open an interesting approach for developing a highly efficient corrosion inhibitor formulation for oil well acidizing based on low-cost computational prediction.

Stability and electronic transport properties for discernible binary (BN)Cx armchair heteronanotubes

We study the electronic transport properties in the (BN) Cx armchair heteronanotubes with binary supercells. The binary supercell consists of a junction of two single (BN) Cx supercells either as the conventional double period structure or two following discernible BN-C patterns. We consider three kinds of discernible binary supercells according to the different orientations of the separated BN armchair cells in both longitudinal and circular directions inside the armchair nanotube. Contrary to the perfect double period structure, the discernible binary (BN)Cx armchair heteronanotube behaves either as a semiconductor or a metal, according to the resulted backscattering effects inside the binary supercell. Moreover, by combining the resulted electronic band structure with the electronic transmission coefficient of the incorporated binary supercell between two perfect leads of carbon nanotubes, we report three universal types of the backscattering effects as fingerprints of the corresponding binary supercell. These types evolve with different regular sequences when the number 'x' of the armchair carbon cells increases inside each kind of the binary (BN) Cx supercell. The energetic stability parameters have also been checked for the BN circular orientations. No evidence for a privileged circular phase has been recorded for the binary (BN)Cx heteronanotubes. The calculations have been performed based on the density functional tight-binding model as implemented in the DFTB ​+ ​software package.

Prediction of anharmonic, condensed-phase IR spectra using a composite approach: Discrete encapsulated chloride hydrates

Composite approaches in which clustering and encapsulation effects are modelled at different levels of theory are capable of reproducing experimental infrared (IR) spectroscopic band centres to within 23 cm−1 (mean absolute deviation), with maximum absolute errors less than 65 cm−1. Anharmonic fundamentals for water stretching modes within isolated clusters may be computed by applying complexation shifts to experimental gas phase water fundamentals, or by empirically scaling harmonic fundamentals computed using “medium accuracy” quantum chemical methods such as dispersion-corrected generalised gradient approximation density functional theories (DFT) or second-order Møller–Plesset perturbation theory (MP2). Environmental effects are modelled using density functional tight binding (DFTB) theories as the difference between harmonic fundamentals in the crystalline environment and in the gas phase. This approach affords a significant improvement in accuracy over conventional approaches in which IR band centres are modelled at a single level of theory. DFT or MP2 calculations on isolated chloride hydrate clusters yield mean and maximum absolute errors of 36 cm−1 and 144 cm−1, respectively. Directly predicting vibrational frequencies in the condensed phase using DFTB models is less accurate again, incurring mean and maximum absolute errors of 93 cm−1 and 204 cm−1, respectively.

Cover Feature: Strong Structural and Electronic Binding of Bovine Serum Albumin to ZnO via Specific Amino Acid Residues and Zinc Atoms (ChemPhysChem 2/2022)

The Cover Feature illustrates how bovine serum albumin binds to polar and non-polar ZnO single-crystal facets via its specific amino acids. Atomic scale calculations using density-functional tight-binding models are shown in the circles for specific binding cases, depending also on ZnO facet orientations. More information can be found in the Article by Hadi Hematian and co-workers.

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential.

Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC6−x; ) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields.

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of 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.

Investigating 3,4-bis(3-nitrofurazan-4-yl)furoxan detonation with a rapidly tuned density functional tight binding model.

We describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with "quantum-accuracy." We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large CxNyOz species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn-Sham density functional theory, which are commonly used for reference data generation.

Caffeine adsorption on hybrid hBN/graphene hexagonal nanoflakes: A density functional tight binding study

The adsorption properties of caffeine molecule on nanoflakes are investigated using density functional tight binding model introduced in DFTB+ package. Sensing ability of hybrid graphene/hBN nanoflake with both nitrogen and boron interfaces and different sizes are compared with graphene and hBN nanoflakes for detecting caffeine molecule. Our findings reveal that proposed hybrid structures are better sensors for caffeine adsorption. In hybrid structures, the final position of the molecule is dependent on the dipole-dipole interaction between the molecule and nanoflake surface. Also, It is worth pointing that growing flake size doesn’t significantly influence on the caffeine adsorption.

Extended tight‐binding quantum chemistry methods

AbstractThis review covers a family of atomistic, mostly quantum chemistry (QC) based semiempirical methods for the fast and reasonably accurate description of large molecules in gas and condensed phase. The theory is derived from a density functional (DFT) perturbation expansion of the electron density in fluctuation terms to various orders similar to the original density functional tight binding model. The term “eXtended” in their name (xTB) emphasizes the parameter availability for almost the entire periodic table of elements (Z ≤ 86) and improvements of the underlying theory regarding, for example, the atomic orbital basis set, the level of multipole approximation and the treatment of the important electrostatic and dispersion interactions. A common feature of most members is their consistent parameterization on accurate gas phase theoretical reference data for geometries, vibrational frequencies and noncovalent interactions, which are the primary properties of interest in typical applications to systems composed of up to a few thousand atoms. Further specialized versions were developed for the description of electronic spectra and corresponding response properties. Besides a provided common theoretical background with some important implementation details in the efficient and free xtb program, various benchmarks for structural and thermochemical properties including (transition‐)metal systems are discussed. The review is completed by recent extensions of the model to the force‐field (FF) level as well as its application to solids under periodic boundary conditions. The general applicability together with the excellent cost‐accuracy ratio and the high robustness make the xTB family of methods very attractive for various fields of computer‐aided chemical research.This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Electronic Structure Theory > Semiempirical Electronic Structure Methods Software > Quantum Chemistry

Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials.

Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. Here we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties.

Reaction Rates in Nitromethane under High Pressure from Density Functional Tight Binding Molecular Dynamics Simulations.

We use density functional tight binding (DFTB) molecular dynamics (MD) simulations to determine the reaction rates of nitromethane CH3NO2 (NM) under high pressure (P = 14-28 GPa), and temperature (T = 1450-1850 K). DFTB-MD simulations performed with the same initial conditions (P0, T0) reveal a stochastic behavior, both in terms of reaction times and chemical paths. By running series of MD simulations, we are able to obtain average reaction times with quantified errors and devise a simple two-step model for NM explosion: ignition/explosion. While our model bypasses the chemical complexity due to the numerous reaction paths and intermediates observed during reactions, the chemistry is accounted for via the accurate parameterization of the DFTB model, and our results suggest a single main reaction pathway for the pressure range considered here, dominated in the earlier stages by the formation of the aci-ion, CH2NOO-. By fitting our data to a Frank-Kamenetskii model, we extract prefactors and pressure-independent activation energies and volumes for the ignition and explosion stages. A two-step model is then built and compared to experimental observations. Single and two-step Arrhenius models are also provided for comparison with literature data. This work presents an efficient way of investigating the reactivity of high explosives by performing electronic structure-based MD simulations and provides reaction rates for simplified models that can be implemented into hydrocodes.

Ranking the Drop-Weight Impact Sensitivity of Common Explosives Using Arrhenius Chemical Rates Computed from Quantum Molecular Dynamics Simulations.

Drop-weight impact tests are used routinely to characterize the handling safety of explosives. Numerous studies have sought to connect various physical and chemical properties of the energetic molecules and materials to their measured impact sensitivities. Wenograd in the early 1960s demonstrated that there is a strong dependency of the drop-heights on the critical temperatures required for explosives to undergo prompt reactions. Reactive quantum molecular dynamics simulations with the lanl31 density functional tight binding model have been used to compute the delay time before the thermal explosion of the secondary explosives erythritol tetranitrate (ETN), pentaerythritol tetranitrate (PETN), cyclotrimethylene trinitramine (RDX), cyclotetramethylene tetranitramine (HMX), trinitrotolune (TNT), and 3,3'-diamino-4,4'-azoxyfurazan (DAAF) as a function of the initial temperature and pressure. The delay time to explosion data are consistent with Arrhenius chemical kinetics, which is expected for thermally activated processes in materials and in accord with experimental measurements. The critical temperatures required for the materials to undergo prompt explosions display the same dependence on drop height as was observed by Wenograd. Hence, quantum-based reactive molecular dynamics simulations are potentially a tool for ranking the drop-weight impact sensitivity and handling safety of explosives.

Performances of Density Functional Tight-Binding Methods for Describing Ground and Excited State Geometries of Organic Molecules.

We present a benchmark of the performances of the density functional tight-binding model (DFTB) and its time-dependent counterpart (TD-DFTB) in describing both the ground state (GS) and excited state (ES) geometries of a panel of 30 organic molecules. Thanks to high-level wave function reference calculations, we are able to quantitatively assess the strengths and weaknesses of four DFTB models, using either second- or third-order self-consistent charge procedures, as well as different sets of parameters. The performances of the different DFTB models are found to be largely dependent on the type of bond considered, but the global mean absolute error remains acceptable for such "cheap" calculations, as it slightly exceeds the one obtained with DFT (PBE and B3LYP) or CC2 models for the GS of the same set of compounds. When considering the ES of the molecules, the TD-DFTB errors are surprisingly not systematically larger than their GS counterparts. However, the trends, either when going from the GS to the ES or within a homologous chemical series, are less consistently reproduced with TD-DFTB than with the considered ab initio models. This work therefore validates the use of TD-DFTB for describing ES geometries while highlighting that care has to be applied when looking at subtle variations.

Correcting long-range electrostatics in DFTB.

We demonstrate that the atom-based charge model implemented in the current versions of the density functional tight binding (DFTB) method fails to reproduce the correct charge distribution of a range of systems, including homonuclear molecules, graphene, and nanotubes, resulting in serious distortions in the electrostatic interactions for such systems caused by the missing quadrupole moments. In particular, this failure seriously impacts the long- and medium-range interaction energies of the DFTB plus dispersion (DFTB-D) model, leading to incorrect predictions of translational or rotational barriers in such systems. We show explicitly on examples of H2 and N2 that correct quadrupole moments-and consequently correct electrostatic interactions-can be restored in such systems by adding additional bond (ghost) sites to the homonuclear molecules. Attempts to determine the point charges associated with the additional sites using the usual Mulliken population analysis lead to unphysical results. Instead, these charges can be determined using the actual DFTB densities used in the parameterization process. For homonuclear molecules, we propose an extension to the DFTB-D model by adding charges that reproduce the physically correct quadrupolar charge distribution. The resulting DFTB-D-Q model greatly improves the rotational barriers for interactions of molecular hydrogen and nitrogen with benzene.

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians.

Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models. The DFTB layer takes, as input, Hamiltonian matrix elements generated from earlier layers and produces, as output, electronic properties from self-consistent field solutions of the corresponding DFTB Hamiltonian. Backpropagation enables efficient training of the model to target electronic properties. Two types of input to the DFTB layer are explored, splines and feed-forward neural networks. Because overfitting can cause models trained on smaller molecules to perform poorly on larger molecules, regularizations are applied that penalize nonmonotonic behavior and deviation of the Hamiltonian matrix elements from those of the published DFTB model used to initialize the model. The approach is evaluated on 15 700 hydrocarbons by comparing the root-mean-square error in energy and dipole moment, on test molecules with eight heavy atoms, to the error from the initial DFTB model. When trained on molecules with up to seven heavy atoms, the spline model reduces the test error in energy by 60% and in dipole moments by 42%. The neural network model performs somewhat better, with error reductions of 67% and 59%, respectively. Training on molecules with up to four heavy atoms reduces performance, with both the spline and neural net models reducing the test error in energy by about 53% and in dipole by about 25%.

Generating Converged Accurate Free Energy Surfaces for Chemical Reactions with a Force-Matched Semiempirical Model.

We demonstrate the capability of creating robust density functional tight binding (DFTB) models for chemical reactivity in prebiotic mixtures through force matching to short time scale quantum free energy estimates. Molecular dynamics using density functional theory (DFT) is a highly accurate approach to generate free energy surfaces for chemical reactions, but the extreme computational cost often limits the time scales and range of thermodynamic states that can feasibly be studied. In contrast, DFTB is a semiempirical quantum method that affords up to a thousandfold reduction in cost and can recover DFT-level accuracy. Here, we show that a force-matched DFTB model for aqueous glycine condensation reactions yields free energy surfaces that are consistent with experimental observations of reaction energetics. Convergence analysis reveals that multiple nanoseconds of combined trajectory are needed to reach a steady-fluctuating free energy estimate for glycine condensation. Predictive accuracy of force-matched DFTB is demonstrated by direct comparison to DFT, with the two approaches yielding surfaces with large regions that differ by only a few kcal mol-1.

Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen.

New parametrizations for semiempirical density functional tight binding (DFTB) theory have been developed by the numerical optimization of adjustable parameters to minimize errors in the atomization energy and interatomic forces with respect to ab initio calculated data. Initial guesses for the radial dependences of the Slater-Koster bond integrals and overlap integrals were obtained from minimum basis density functional theory calculations. The radial dependences of the pair potentials and the bond and overlap integrals were represented by simple analytic functions. The adjustable parameters in these functions were optimized by simulated annealing and steepest descent algorithms to minimize the value of an objective function that quantifies the error between the DFTB model and ab initio calculated data. The accuracy and transferability of the resulting DFTB models for the C, H, N, and O system were assessed by comparing the predicted atomization energies and equilibrium molecular geometries of small molecules that were not included in the training data from DFTB to ab initio data. The DFTB models provide accurate predictions of the properties of hydrocarbons and more complex molecules containing C, H, N, and O.