Accuracy Of Density Functional Theory Research Articles

Understanding the effect of density functional choice and van der Waals treatment on predicting the binding configuration, loading, and stability of amine-grafted metal organic frameworks.

Metal organic frameworks (MOFs) are crystalline, three-dimensional structures with high surface areas and tunable porosities. Made from metal nodes connected by organic linkers, the exact properties of a given MOF are determined by node and linker choice. MOFs hold promise for numerous applications, including gas capture and storage. M2(4,4'-dioxidobiphenyl-3,3'-dicarboxylate)-henceforth simply M2(dobpdc), with M = Mg, Mn, Fe, Co, Ni, Cu, or Zn-is regarded as one of the most promising structures for CO2 capture applications. Further modification of the MOF with diamines or tetramines can significantly boost gas species selectivity, a necessity for the ultra-dilute CO2 concentrations in the direct-air capture of CO2. There are countless potential diamines and tetramines, paving the way for a vast number of potential sorbents to be probed for CO2 adsorption properties. The number of amines and their configuration in the MOF pore are key drivers of CO2 adsorption capacity and kinetics, and so a validation of computational prediction of these quantities is required to suitably use computational methods in the discovery and screening of amine-functionalized sorbents. In this work, we study the predictive accuracy of density functional theory and related calculations on amine loading and configuration for one diamine and two tetramines. In particular, we explore the Perdew-Burke-Ernzerhof (PBE) functional and its formulation for solids (PBEsol) with and without the Grimme-D2 and Grimme-D3 pairwise corrections (PBE+D2/3 and PBEsol+D2/3), two revised PBE functionals with the Grimme-D2 and Grimme-D3 pairwise corrections (RPBE+D2/3 and revPBE+D2/3), and the nonlocal van der Waals correlation (vdW-DF2) functional. We also investigate a universal graph deep learning interatomic potential's (M3GNet) predictive accuracy for loading and configuration. These results allow us to identify a useful screening procedure for configuration prediction that has a coarse component for quick evaluation and a higher accuracy component for detailed analysis. Our general observation is that the neural network-based potential can be used as a high-level and rapid screening tool, whereas PBEsol+D3 gives a completely qualitatively predictive picture across all systems studied, and can thus be used for high accuracy motif predictions. We close by briefly exploring the predictions of relative thermal stability for the different functionals and dispersion corrections.

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.

Discovery of Stable Surfaces with Extreme Work Functions by High‐Throughput Density Functional Theory and Machine Learning

AbstractThe work function is the key surface property that determines the energy required to extract an electron from the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. This work presents a high‐throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that are created from 3,716 bulk materials. The number of calculated surface properties surpasses the previously largest database by a factor of ≈27. Several surfaces with an ultra‐low (<2 eV) and ultra‐high (>7 eV) work function are identified. Specifically, the (100)‐Ba‐O surface of BaMoO3 and the (001)‐F surface of Ag2F have record‐low (1.25 eV) and record‐high (9.06 eV) steady‐state work functions. Based on this database a physics‐based approach to featurize surfaces is utilized to predict the work function. The random forest model achieves a test mean absolute error (MAE) of 0.09 eV, comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function (≈ 105 faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion and electronic device applications.

Moment tensor potential for static and dynamic investigations of screw dislocations in bcc Nb

A new machine-learning interatomic potential, specifically a moment tensor potential (MTP), is developed for the study of screw-dislocation properties in body-centered-cubic (bcc) Nb in the thermally- and stress-assisted temperature regime. Importantly, configurations with straight screw dislocations and with kink pairs are included in the training set. The resulting MTP reproduces with near density-functional theory (DFT) accuracy a broad range of physical properties of bcc Nb, in particular, the Peierls barrier and the compact screw-dislocation core structure. Moreover, it accurately reproduces the energy of the easy core and the twinning-anti-twinning asymmetry of the critical resolved shear stress (CRSS). Thereby, the developed MTP enables large-scale molecular dynamics simulations with near DFT accuracy of properties such as for example the Peierls stress, the critical waiting time for the onset of screw dislocation movement, atomic trajectories of screw dislocation migration, as well as the temperature dependence of the CRSS. A critical assessment of previous results obtained with classical embedded atom method potentials thus becomes possible.

Ferroelectric Phase Transition in Barium Titanate Revisited with Ab Initio Molecular Dynamics.

The ferroelectric phase transition of the perovskite barium titanate as well as its technical importance regarding the switching of respective polar properties is well known and has been thoroughly studied, both experimentally and on theoretical grounds. While details about the phase diagram as well as transition temperatures are experimentally well known, the theoretical approaches still face difficulties in contributing a detailed description of these phase transitions. Within this work, a new methodological approach is introduced to revisit the ferroelectric phase transition with first-principles methods. With the chosen ab initio molecular dynamics (AIMD) method in combination with the applied NpT ensemble, we are able to join the accuracy of density functional theory (DFT) with ambient conditions, realized using a thermostat and barostat in an MD simulation. The derived phase diagram confirms recent corrections in the theoretical models and reproduces the phase boundary pressure dependence of TC. In conclusion of the statistical atomistic dynamics, the nature of the transition can be described in a more detailed way. In addition, this work paves the way towards locally patterned piezoelectrica by means of acoustic standing waves as well as piezoelectrically induced acoustic resonators.

Determining the mechanical and decomposition properties of high energetic materials (α-RDX, β-HMX, and ε-CL-20) using a neural network potential.

Molecular simulations of high energetic materials (HEMs) are limited by efficiency and accuracy. Recently, neural network potential (NNP) models have achieved molecular simulations of millions of atoms while maintaining the accuracy of density functional theory (DFT) levels. Herein, an NNP model covering typical HEMs containing C, H, N, and O elements is developed. The mechanical and decomposition properties of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by employing the molecular dynamics (MD) simulations based on the NNP model. The calculated results show that the mechanical properties of α-RDX, β-HMX, and ε-CL-20 agree with previous experiments and theoretical results, including cell parameters, equations of state, and elastic constants. In the thermal decomposition simulations, it is also found that the initial decomposition reactions of the three crystals are N-NO2 homolysis, corresponding radical intermediates formation, and NO2-induced reactions. This decomposition trajectory is mainly divided into two stages separating from the peak of NO2: pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements in this work is an alternative reactive force field for RDX, HMX, and CL-20 HEMs, and it opens up new potential for future kinetic study of nitramine explosives.

Neural Network Approach for a Rapid Prediction of Metal-Supported Borophene Properties.

We developed a high-dimensional neural network potential (NNP) to describe the structural and energetic properties of borophene deposited on silver. This NNP has the accuracy of density functional theory (DFT) calculations while achieving computational speedups of several orders of magnitude, allowing the study of extensive structures that may reveal intriguing moiré patterns or surface corrugations. We describe an efficient approach to constructing the training data set using an iterative technique known as the "adaptive learning approach". The developed NNP is able to produce, with excellent agreement, the structure, energy, and forces obtained at the DFT level. Finally, the calculated stability of various borophene polymorphs, including those not initially included in the training data set, shows better stabilization for ν ∼ 0.1 hole density, and in particular for the allotrope α . The stability of borophene on the metal surface is shown to depend on its orientation, implying structural corrugation patterns that can be observed only from long-time simulations on extended systems. The NNP also demonstrates its ability to simulate vibrational densities of states and produce realistic structures with simulated STM images closely matching the experimental ones.

Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials.

The properties of two-dimensional materials are strongly affected by defects that are often present in considerable numbers. In this study, we investigate the diffusion and coalescence of monovacancies in phosphorene using molecular dynamics (MD) simulations accelerated by high-dimensional neural network potentials. Trained and validated with reference data obtained with density functional theory (DFT), such surrogate models provide the accuracy of DFT at a much lower cost, enabling simulations on time scales that far exceed those of first-principles MD. Our microsecond long simulations reveal that monovacancies are highly mobile and move predominantly in the zigzag rather than armchair direction, consistent with the energy barriers of the underlying hopping mechanisms. In further simulations, we find that monovacancies merge into energetically more stable and less mobile divacancies following different routes that may involve metastable intermediates.

Edge Conductivity in PtSe2 Nanostructures

PtSe2 is a promising 2D material for nanoelectromechanical sensing and photodetection in the infrared regime. One of its most compelling features is the facile synthesis at temperatures below 500 °C, which is compatible with current back‐end‐of‐line semiconductor processing. However, this process generates polycrystalline thin films with nanoflake‐like domains of 5–100 nm size. To investigate the lateral quantum confinement effect in this size regime, a deep neural network is trained to obtain an interatomic potential at density functional theory accuracy and it is used to model ribbons, surfaces, nanoflakes, and nanoplatelets of PtSe2 with lateral widths between 5 and 15 nm. Which edge terminations are the most stable are determined and evidence is found that the electrical conductivity is localized on the edges for lateral sizes below 10 nm. This suggests that the transport channels in thin films of PtSe2 might be dominated by networks of edges, instead of transport through the layers themselves.

Consistent density functional theory-based description of ion hydration through density-corrected many-body representations

Delocalization error constrains the accuracy of density functional theory in describing molecular interactions in ion–water systems. Using Na+ and Cl− in water as model systems, we calculate the effects of delocalization error in the SCAN functional for describing ion–water and water–water interactions in hydrated ions, and demonstrate that density-corrected SCAN (DC-SCAN) predicts n-body and interaction energies with an accuracy approaching coupled cluster theory. The performance of DC-SCAN is size-consistent, maintaining an accurate description of molecular interactions well beyond the first solvation shell. Molecular dynamics simulations at ambient conditions with many-body MB-SCAN(DC) potentials, derived from the many-body expansion, predict the solvation structure of Na+ and Cl− in quantitative agreement with reference data, while simultaneously reproducing the structure of liquid water. Beyond rationalizing the accuracy of density-corrected models of ion hydration, our findings suggest that our unified density-corrected MB formalism holds great promise for efficient DFT-based simulations of condensed-phase systems with chemical accuracy.

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning.

Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid, and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this nonparametric force field produces properties with excellent accuracy as well as exhibits computationally tractable efficiency. The force field is capable of allowing us to simulate the solid-liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films.

Bond dissociation energies of the fifth‐row elements (InI): A quantum theoretical benchmark study

AbstractThe bond dissociation energies (BDE) of most main‐group elements have been accurately measured. However, the BDE values for heavy elements, particularly those from the fifth period (InI), are still missing or poorly validated. This study aims to identify the most accurate computational methods for calculating BDE values of compounds containing fifth‐row elements, including In, Sn, Sb, Te, and I, with a focus on readily accessible methods in software packages. The investigation involved a benchmark study using density functional theory (DFT), in addition to the 2nd order Møller–Plesset perturbation theory (MP2) and the coupled cluster with single, double, and perturbative triple excitations CCSD(T). The DFT functionals used in the study include APFD, B3LYP, B3LYP‐D3, B3P86, B97‐D3, BHandH, HSEH1PBE, M06‐2X, MN12‐SX, MN15‐L, and TPSSH. The functionals were carefully selected to cover some popular functionals as well as to cover all levels of the Jacob's ladder of DFT accuracy. The computed BDE values were compared with experimental values, and the results were filtered to remove any possible outliers. The statistical errors (MAPE, RMSE, and Pearson's) were then calculated and used to assess the performance of the methods.

Implementation of the meta-GGA exchange-correlation functional in numerical atomic orbital basis: With systematic testing on SCAN, rSCAN, and r2SCAN functionals.

Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the exchange-correlation functional. By including the kinetic energy density τ, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better accuracy and flexibility while retaining the efficiency of semi-local functionals. For example, the strongly constrained and appropriately normed (SCAN) meta-GGA functional has been proven to yield accurate results for solid and molecular systems. We implement meta-GGA functionals with both numerical atomic orbitals and plane wave bases in the ABACUS package. Apart from the exchange-correlation potential, we also discuss the evaluation of force and stress. To validate our implementation, we perform finite-difference tests and convergence tests with the SCAN, rSCAN, and r2SCAN meta-GGA functionals. We further test water hexamers, weakly interacting molecules from the S22 dataset, as well as 13 semiconductors using the three functionals. The results show satisfactory agreement with previous calculations and available experimental values.

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy

The establishment of reliable materials genome databases involving the thermophysical properties of liquid metals and alloys promotes the progress of materials research and development, whereas acquiring these properties imposes great challenges on experimental investigation. Here, we proposed a deep learning method and achieved a deep neural network (DNN) interatomic potential for the entire Ti–Ni–Cr–Al system from pure metals to quaternary alloys. This DNN potential exhibited sufficient temperature and compositional transformability which extended beyond the training and provided the prediction of the liquid structure and thermophysical properties for metallic materials with both density functional theory accuracy and classic molecular dynamics efficiency. The predicted results agreed well with the reported experimental data. This work opens a feasible way to address the challenges of rapidly and accurately acquiring thermophysical properties data for liquid pure metals and multicomponent alloys, covering a broad temperature range from superheated to undercooled state.

Simulation studies of the stability and growth kinetics of Pt-Sn phases using a machine learning interatomic potential

The thermodynamic stability and growth kinetics of Pt-Sn phases are investigated by atomistic simulations utilizing a neural-network machine learning (NN-ML) interatomic potential. The physical properties of Pt-Sn crystalline phases described by the NN-ML interatomic potential, such as equation of states, formation energy convex hull, and phonon vibrational spectrum, are in in accord well with first-principles calculations and experimental data. The calculations of temperature dependent Gibbs free energies of the crystalline Pt-Sn phases by the NN-ML potential are in the efficiency of empirical interatomic potentials and accuracy of density functional theory (DFT). The developed NN-ML potential is also used to investigate the structures and dynamics of liquid phases of Pt-Sn alloys by molecular dynamics (MD) simulations. The crystallization of PtSn and Pt3Sn phases from the solid–liquid interface are also studied by MD simulations using the NN-ML potential. The results obtained from our studies provide useful insight into thermodynamics stability and growth kinetics of Pt-Sn binary phases.

Rotationally Inelastic Scattering Dynamics of NO from Ag(111): Influence of Interaction Potentials

State-to-state molecular scattering from surfaces represents a sensitive probe of the molecule–surface interaction potential. For NO scattering from Ag(111), a benchmark system for studying gas–surface energy transfer, abundant experimental data have provided us a great testing ground to validate the accuracy of first-principles description of molecule–surface interactions. Here, we use an accurate neural network fitting method to construct two high-dimensional potential energy surfaces (PESs) based on density functional theory (DFT) single points using PBE and revPBE functionals, respectively, for describing the rotationally inelastic scattering of NO from Ag(111). Quasi-classical trajectory calculations based on the two PESs have been compared with a diversity of experimental data and the influence of scattering angle, initial molecular orientation, and incidence energy on the rotational state distribution is discussed. It is found in general that revPBE-based results agree better than PBE-based ones with the measured scattering angle and final rotational state distributions, capturing reasonably the rotational rainbow feature at a small incidence energy and the steric effect. But neither PES reproduces all experimental data quantitatively. The different performance of the two PESs can be attributed to their difference in the attractiveness, anisotropy, and corrugation in the entrance channel. Our results suggest the need of further improvement of the accuracy of DFT with commonly used density functionals toward a chemically accurate description of molecule–surface interactions.

Accelerating Non-Empirical Structure Determination of Ziegler–Natta Catalysts with a High-Dimensional Neural Network Potential

The determination of catalyst nanostructures with first-principles accuracy using genetic algorithms (GA) is very demanding due to the cubic scaling of the computational cost of density functional theory (DFT) calculations. Here, we demonstrate, for the case of Ziegler–Natta MgCl2/TiCl4 nanoplates, how this structure determination can be accelerated by employing a high-dimensional neural network potential (HDNNP) of essentially DFT accuracy. First, when building HDNNPs for MgCl2/TiCl4 clusters with computationally tractable sizes, we found that the structural diversity in the training set is crucial for obtaining HDNNPs reliably describing the large variety of structures generated by GA. The resulting HDNNPs dramatically accelerated the structure determination while yielding results consistent with DFT. Subsequently, we developed a multistep adaptive procedure to construct a HDNNP for MgCl2/TiCl4 clusters consistent in size and TiCl4 coverage with experiments where prior DFT results were scarcely collected. The structure determination and analyses underline the importance of system size and composition in order to predict some experimentally known facts such as the surface morphology and population of isospecific sites.

Benchmarking First-Principles Reaction Equilibrium Composition Prediction.

The availability of thermochemical properties allows for the prediction of the equilibrium compositions of chemical reactions. The accurate prediction of these can be crucial for the design of new chemical synthesis routes. However, for new processes, these data are generally not completely available. A solution is the use of thermochemistry calculated from first-principles methods such as Density Functional Theory (DFT). Before this can be used reliably, it needs to be systematically benchmarked. Although various studies have examined the accuracy of DFT from an energetic point of view, few studies have considered its accuracy in predicting the temperature-dependent equilibrium composition. In this work, we collected 117 molecules for which experimental thermochemical data were available. From these, we constructed 2648 reactions. These experimentally constructed reactions were then benchmarked against DFT for 6 exchange-correlation functionals and 3 quality of basis sets. We show that, in reactions that do not show temperature dependence in the equilibrium composition below 1000 K, over 90% are predicted correctly. Temperature-dependent equilibrium compositions typically demonstrate correct qualitative behavior. Lastly, we show that the errors are equally caused by errors in the vibrational spectrum and the DFT electronic ground state energy.

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.

On-the-fly machine learning potential accelerated accurate prediction of lattice thermal conductivity of metastable silicon crystals

In this paper, we propose a convenient strategy to accelerate the evaluation of lattice thermal conductivity through combining phonon Boltzmann transport equation (PBTE) and on-the-fly machine learning potential (FMLP). The thermal conductivity of diamond silicon ($d\text{\ensuremath{-}}\mathrm{Si}$) is evaluated firstly by density functional theory (DFT), FMLP, and empirical potential with PBTE, respectively. The results demonstrate the proposed strategy integrates the prediction accuracy of DFT and computational speed of empirical potential, breaking the dilemma of traditional thermal conductivity assessment schemes. Based on this, the efficient strategy is applied to predict thermal conductivity of 102 low-energy metastable silicon crystals with energies between $d\text{\ensuremath{-}}\mathrm{Si}$ and experimentally ${\mathrm{Si}}_{24}$. Among them, the $Cmcm\text{\ensuremath{-}}{\mathrm{Si}}_{16}, P6/mmm\text{\ensuremath{-}}{\mathrm{Si}}_{36}$-2, $Pnma\text{\ensuremath{-}}{\mathrm{Si}}_{32}$-2 are predicted to host lowest lattice thermal conductivity in $xx$ (8.213 ${\mathrm{Wm}}^{\ensuremath{-}1}{\mathrm{K}}^{\ensuremath{-}1}$), $yy$ (10.917 ${\mathrm{Wm}}^{\ensuremath{-}1}{\mathrm{K}}^{\ensuremath{-}1}$), and $zz$ (11.807 ${\mathrm{Wm}}^{\ensuremath{-}1}{\mathrm{K}}^{\ensuremath{-}1}$) directions, respectively. Such low lattice thermal conductivity benefits from the combined effect of low phonon group velocity and intense phonon scattering caused by distorted $s{p}^{3}$ hybrid states in these metastable silicon crystals. The findings presented in this work provide new candidates and insights of silicon-based materials with ultra-low thermal conductivity, which will greatly expand the applications in thermoelectric and thermal insulation fields.