Energy Density Functional Theory Research Articles

NablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset.

Electronic wave function calculation is a fundamental task of computational quantum chemistry. Knowledge of the wave function parameters allows one to compute physical and chemical properties of molecules and materials. Unfortunately, it is infeasible to compute the wave functions analytically even for simple molecules. Classical quantum chemistry approaches such as the Hartree-Fock method or density functional theory (DFT) allow to compute an approximation of the wave function but are very computationally expensive. One way to lower the computational complexity is to use machine learning models that can provide sufficiently good approximations at a much lower computational cost. In this work we: (1) introduce a new curated large-scale dataset of electron structures of drug-like molecules, (2) establish a novel benchmark for the estimation of molecular properties in the multi-molecule setting, and (3) evaluate a wide range of methods with this benchmark. We show that the accuracy of recently developed machine learning models deteriorates significantly when switching from the single-molecule to the multi-molecule setting. We also show that these models lack generalization over different chemistry classes. In addition, we provide experimental evidence that larger datasets lead to better ML models in the field of quantum chemistry.

Extremely High Thermal Conductivity of Aligned Polyacetylene Predicted using First-Principles-Informed United-Atom Force Field

Molecular simulations of polymer rely on accurate force fields to describe the inter-atomic interactions. In this work, we use first-principles density functional theory (DFT) calculations to parameterize a united-atom force field for polyacetylene (PA), a conjugated polymer potentially of high thermal conductivity. Different electron correlation functionals in DFT have been tested. Bonding interactions for the alternating single and double bonds in the conjugated polymer backbone are explicitly described and Class II anharmonic functions are separately parameterized. Bond angle and dihedral interactions are also anharmonic and parameterized against DFT energy surfaces. The established force field is then used in molecular dynamics (MD) simulations to calculate the thermal conductivity of single PA chains and PA crystals with different simulation domain lengths. It is found that the thermal conductivity values of both PA single chains and crystals are very high and are length-dependent. At 790 nm, their respective thermal conductivity values are ~480 Wm-1K-1 and ~320 Wm-1K-1, which are comparatively higher than those of polyethylene (PE), the most thermally conductive polymer fiber measured up-to-date.

Ab initio modeling of H2S dissociative chemisorption on Ag(100).

Natural sulfidation of silver nanomaterials can passivate the surface, while preserving desirable optical and electrical properties, which is beneficial for limiting Ag+ release and cytotoxicity. But little is known at the atomic scale about silver sulfidation mechanisms, particularly on different crystallographic terminations. Using density functional theory (DFT) calculations, we examined the process of H2S sorption and reaction on Ag(100) surfaces relevant to Ag nanowires (AgNWs). DFT energy minimizations predict a strong dissociative chemisorption of H2S on the surface yielding co-adsorbed sulfide and hydrogen atoms in specific surface sites. However, nudged elastic band (NEB) calculations suggest relatively large activation energies for both the first and second dissociation steps, due in part to overcoming the energy to cleave the S-H bond and attendant site migration from an on-top Ag site position to a hollow site position of the bound S atom. The large barriers associated with the dissociative chemisorption reaction for gas-phase H2S points to the importance of including thermochemical contributions and the influence of other components in more complex environmental media such as air or water to help complete the mechanistic picture of silver sulfidation and passivation for realistic systems.

Description of weak-interaction rates within the relativistic energy density functional theory

A new theoretical framework has been established and applied in the calculation of electron capture (EC) and β-decay rates in stellar environment, characterized by high density and temperature. For the description of the nuclear properties, the finite-temperature Hartree Bardeen-Cooper-Schrie_er (FTHBCS) theory based on the relativistic derivative-coupling D3C* interaction is employed. In order to describe the charge-exchange transitions, the finitetemperature proton-neutron quasi-particle random-phase approximation is developed (FT-PNRQRPA) which includes both temperature and pairing correlations. In the FT-HBCS calculations, only the isovector pairing is included, while in the residual interaction of the FT-PNRQRPA both the isovector and isoscalar pairing contribute. In this work, results for EC and β-decay rates are presented in the temperature interval T = 0–1.5 MeV and stellar density ρYe = 107 and 109 g/cm3. Both allowed 0+, 1+ and first-forbidden transitions 0−, 1− and 2− are included in the calculations. It is shown that interplay between pairing correlations and finite-temperature effects can lead to significant changes in rates. It is also important to include de-excitations, i.e. transitions with negative Q-value, that become increasingly significant at higher temperatures especially for p f -shell nuclei.

Microscopic Structure of the Low-Energy Electric Dipole Response of ^{120}Sn.

The microscopic structure of the low-energy electric dipole response, commonly denoted as pygmy dipole resonance (PDR), was studied for ^{120}Sn in a ^{119}Sn(d,pγ)^{120}Sn experiment. Unprecedented access to the single-particle structure of excited 1^{-} states below and around the neutron-separation threshold was obtained by comparing experimental data to predictions from a novel theoretical approach. The novel approach combines detailed structure input from energy-density functional plus quasiparticle-phonon model theory with reaction theory to obtain a consistent description of both the structure and reaction aspects of the process. The presented results show that the understanding of one-particle-one-hole structures of the 1^{-} states in the PDR region is crucial to reliably predict properties of the PDR and its contribution to nucleosynthesis processes.

Passive mode-locking by a Ti2AlN saturable absorber in 1.5 µm region

We conducted an investigation on the saturable absorption properties of Ti2AlN through both experimental material characterizations and a density functional theory energy band calculation. On the basis of the saturable absorption properties of the Ti2AlN MAX phase that was observed in this investigation, a fiberized mode-locker operable in the 1.5-μm spectral region was fabricated with a side polished fiber platform. The modulation depth and saturation intensity of the mode-locker were measured as ~7% and ~91 MW/cm2, respectively. By inserting the mode-locker into an Er3+ doped fiber ring cavity, conventional soliton pulses, which were stably mode-locked, could be generated at ~1557.6 nm.

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy.

We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 106 DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R2 = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R2 = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2-TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for non-covalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/molfor the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset.

Machine-learning potentials enable predictive and tractable high-throughput screening of random alloys

We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of $\ensuremath{\sim}10$ 000 or more training candidate configurations with 50--100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to $\ensuremath{\sim}100$ most distinct configurations---for which DFT energies and forces are computed and on which the potential is ultimately trained. We validate our algorithm for the MoNbTa medium-entropy alloy by showing that the MTP reproduces the DFT $\frac{1}{4}[111]$ unstable stacking fault energy over the entire compositional space up to a few percent. Contrary to state-of-the-art methods, e.g., the coherent potential approximation (CPA) or special quasi-random structures (SQSs), our algorithm naturally accounts for relaxation, is not limited by DFT cell sizes, and opens opportunities to efficiently investigate follow-up problems, such as chemical ordering. In a broader sense, our algorithm can be easily modified to compute related properties of random alloys, for instance, misfit volumes, or grain boundary energies. Moreover, it forms the basis for an efficient construction of MTPs to be used in large-scale simulations of multicomponent systems.

Adsorption of small organic acids and polyphenols on hematite surfaces: Density Functional Theory + thermodynamics analysis

HypothesisThe interactions of organic molecules with mineral surfaces are influenced by several factors such as adsorbate speciation, surface atomic and electronic structure, and environmental conditions. When coupled with thermodynamic techniques, energetics from atomistic modeling can provide a molecular-level picture of which factors determine reactivity. This is paramount for evaluating the chemical processes which control the fate of these species in the environment. ExperimentsInner-sphere adsorption of oxalate and pyrocatechol on (001), (110), and (012) α-Fe2O3 surfaces was modeled using Density Functional Theory (DFT). Unique bidentate binding modes were sampled along each facet to study how different adsorbate and surface factors govern site preference. Adsorption energetics were then calculated using a DFT + thermodynamics approach which combines DFT energies with tabulated data and Nernst-based corrective terms to incorporate different experimental parameters. FindingsInstead of a universal trend, each facet displays a unique factor that dominates site preference based on either strain (001), functional groups (110), or topography (012). Adsorption energies predict favorable inner-sphere adsorption for both molecules but opposite energetic trends with varying pH. Additionally, vibrational analysis was conducted for each system and compared to experimental IR data. The work presented here provides an effective, computational methodology to study numerous adsorption processes occurring at the surface-aqueous interface.

Quasiclassical Direct Dynamics Trajectory Simulations of Organometallic Reactions.

Homogeneous metal-mediated organometallic reactions represent a very large and diverse reaction class. Density functional theory calculations are now routinely carried out and reported for analyzing organometallic mechanisms and reaction pathways. While density functional theory calculations are extremely powerful to understand the energy and structure of organometallic reactions, there are several assumptions in their use and interpretation to define reaction mechanisms and to analyze reaction selectivity. Almost always it is assumed that potential energy structures calculated with density functional theory adequately describe mechanisms and selectivity within the framework of statistical theories, for example, transition state theory and RRKM theory. However, these static structures and corresponding energy landscapes do not provide atomic motion information during reactions that could reveal nonstatistical intermediates without complete intramolecular vibrational redistribution and nonintrinsic reaction coordinate (non-IRC) pathways. While nonstatistical intermediates and non-IRC reaction pathways are now relatively well established for organic reactions, these dynamic effects have heretofore been highly underexplored in organometallic reactions. Through a series of quasiclassical density functional theory direct dynamics trajectory studies, my group has recently demonstrated that dynamic effects occur in a variety of fundamental organometallic reactions, especially bond activation reactions. For example, in the C-H activation reaction between methane and [Cp*(PMe3)IrIII(CH3)]+, while the density functional theory energy landscape showed a two-step oxidative cleavage and reductive coupling mechanism, trajectories revealed a mixture of this two-step mechanism and a dynamic one-step mechanism that skipped the [Cp*(PMe3)IrV(H)(CH3)2]+ intermediate. This study also showed that despite a methane σ-complex being located on the density functional theory surface before oxidative cleavage and after reductive coupling, this intermediate is always skipped and should not be considered an intermediate during reactive trajectories. For non-IRC reaction pathways, quasiclassical direct dynamics trajectories showed that for the isomerization of [Tp(NO)(PMe3)W(η2-benzene)] to [Tp(NO)(PMe3)W(H)(Ph)], there are many dynamic reaction pathway connections due to a relatively flat energy landscape and π coordination is not necessary for C-H bond activation through oxidative cleavage. Trajectories also showed that dynamic effects are important in selectivity for ethylene C-H activation versus π coordination in reaction with Cp(PMe3)2Re, and trajectories provide a more quantitative model of selectivity than transition state theory. Quasiclassical trajectories examining Au-catalyzed monoallylic diol cyclizations showed dynamic coupling of several reaction steps that include alkoxylation π bond addition, proton shuttling, and water elimination reaction steps. Overall, these studies highlight the need to use direct dynamics trajectory simulations to consider atomic motion during reactions to understand organometallic reaction mechanisms and selectivity.

Practical treatment of singlet oxygen with density-functional theory and the multiplet-sum method

Singlet oxygen (O2) comes in two flavors -- namely the dominant lower-energy a 1 Delta g state and the higher-energy shorter-lived b 1 Sigma + g state -- and plays a key role in many photochemical and photobiological reactions. For this reason, and because of the large size of the systems treated, many papers have appeared with density-functional theory (DFT) treatments of the reactions of 1 O 2 with different chemical species. The present work serves as a reminder that the common assumption that it is enough to fix the spin multipicity as unity is not enough to insure a correct treatment of singlet oxygen. We review the correct group theoretical treatment of the three lowest energy electronic states of O 2 which, in the case of 1 O 2 is often so badly explained in the relevant photochemical literature that the explanation borders on being incorrect and prevents, rather than encourages, a correct treatment of this interesting and important photochemical species. We then show how many electronic structure programs, such as a freely downloadable and personal-computer compatible Linux version of deMon2k, may be used, together with the multiplet sum method (MSM), to obtain a more accurate estimation of the potential energy curves (PECs) of the two 1 O 2 states. Various strengths and weaknesses of different DFAs emerge through our application of the MSM method. In particular, the quality of the a 1 Delta g excitation energy reflects how well functionals are able to describe the spin-flip energy in DFT while the quality of the b 1 Sigma + g excitation energy reflects how well functionals are able to describe the spin-pairing energy in DFT. Finally we note that improvements in DFT-based excited-state methods will be needed to describe the full PECs of 1 O 2 including both the equilibrium bond lengths and dissociation behavior.

Assessment of sulfobutylether-beta-cyclodextrin as a promising Fluorometholone molecule container: DFT, Docking, Molecular dynamics and MM-PBSA free energy calculations

ABSTRACT In the present work, the preferential binding mode of Fluorometholone (FLU) drug into the nanopores of two cyclodexterins, b-cyclodextrin (b-CD) and sulfobutylether-b-cyclodextrin (SEB-b-CD), are theoretically investigated using density functional theory (DFT), Docking, the molecular dynamics (MD) simulation and MM-PBSA free energy calculations. The computational results reveal that hydrogen bonds (HBs) and van der Waals (vdW) interactions have the significant roles in the stability of FLU into the cavity of b-CD and SBE-b-CD structures in the aqueous phase. It is found that the complexing ability of FLU is significantly increased from -62.245 kJ/mol in native b-CD to -89.677 kJ/mol in SEB-b-CD by introducing sulfobutylether groups to the hydroxyl groups of the macromolecule. In addition, a remarkable enhancement of the solubility of SBE-β-CD, compared to the native β-cyclodextrin, is observed. The inclusion process of FLU drug into SEB-β-CD improves the polarity of FLU@SBE-β-CD complex and enhances its solubility. Furthermore, the conformational stability of the top-scored FLU@β-CD and FLU@SEB-β-CD docked models is analysed by molecular dynamics simulation. The MM-PBSA freeenergy approach approves more binding affinity of FLU drug into the SEB-β-CD cavity. The obtained data aim to explore the potential application of SEB-β-CD macromolecule for designing and developing pharmacological formulations.

Oxygen ionic transport in LaInO3 and LaIn0.5Zn0.5O2.75 perovskites: Theory and experiment

The new oxygen-deficient orthorhombic perovskite LaIn0.5Zn0.5O2.75 was obtained by solid-state synthesis. Ionic transport properties were studied by a combined theoretical approach consisting of geometrical-topological analysis, bond valence site energy modeling and density functional theory (DFT) calculations. The DFT calculations showed that oxygen migration energy is by 0.2 eV lower for zinc-containing perovskite compared to pure LaInO3. The conductivity was measured in the temperature range of 400–1000 °C and at various oxygen partial pressures 10−18–2·10−1 atm. LaIn0.5Zn0.5O2.75 was found to be pure oxide-ion conductor (σ ≈ 10−5 S/cm) at T < 550 °C in air with the experimental ionic activation energy of 0.79 eV compared to 0.97 eV for LaInO3, which is in agreement with our theoretical prediction. With increasing the temperature the share of the hole conductivity of LaIn0.5Zn0.5O2.75 increased.

Vanillin chitosan miscible hydrogel blends and their prospects for 3D printing biomedical applications

In the present study, chitosan (CS) reacted with vanillin through a Schiff base reaction forming the vanillin-CS (VACS) derivative. FTIR and 1H NMR spectra confirmed the derivatization of CS, the enhanced swelling behavior was long-established while XRD measurement stated the semicrystalline nature of the VACS derivative. In a further step, blends between CS and VACS were prepared in ratios CS/VACS 90/10 up to 10/90 w/w and the formation of hydrogen bonds was noticed through FTIR and XRD measurements. Structural optimizations were performed within the framework of density functional theory and interaction energies Eint were calculated. Collectively, these results along with viscosity measurements and SEM images prove the miscibility of CS/VACS blends. In the optimum CS/VACS ratios, inks for 3D printing application were prepared in different concentrations (3%w/v, 4%w/v, 5%w/v, 6%w/v). The augmentation of the samples' viscosity values influenced by the polymeric concentration was assessed while their thereafter printing application was conducted.

Nano-saturn and ExBox systems: Dominant role of steric, exchange-correlation, and electrostatic effects

To appreciate the origin of host-guest (HG) interactions in nano-Saturn and ExBox systems, we propose to use the density functional theory energy partitioning schemes. One approach includes the kinetic, electrostatic, and exchange-correlation energies, while the contributions of another partitioning scheme are the steric, electrostatic, and quantum effects. It is shown that the steric and exchange-correlation effects as well as the electrostatic interactions are dominant factors contributing to the total interaction energies. Furthermore, an alternative energy partitioning scheme including steric, exchange-correlation, and electrostatic energies is also proposed to accurately accounting for HG interactions in nano-Saturn and ExBox systems.

An automatized workflow from molecular dynamic simulation to quantum chemical methods to identify elementary reactions and compute reaction constants.

We present an automatized workflow which, starting from molecular dynamics simulations, identifies reaction events, filters them, and prepares them for accurate quantum chemical calculations using, for example, Density Functional Theory (DFT) or Coupled Cluster methods. The capabilities of the automatized workflow are demonstrated by the example of simulations for the combustion of some polycyclic aromatic hydrocarbons (PAHs). It is shown how key elementary reaction candidates are filtered out of a much larger set of redundant reactions and refined further. The molecular species in question are optimized using DFT and reaction energies, barrier heights, and reaction rates are calculated. The setup is general enough to include at this stage configurational sampling, which can be exploited in the future. Using the introduced machinery, we investigate how the observed reaction types depend on the gas atmosphere used in the molecular dynamics simulation. For the re-optimization on the DFT level, we show how the additional information needed to switch from reactive force-field to electronic structure calculations can be filled in and study how well ReaxFF and DFT agree with each other and shine light on the perspective of using more accurate semi-empirical methods in the MD simulation.

In-situ quantification and density functional theory elucidation of phase transformation in carbon steel during quenching and partitioning

Phase transformation in low alloyed Fe-C steels during quenching and partitioning (Q&P), though authentically attributed to carbon diffusion, is scarcely quantified experimentally in terms of microstructural evolution and lack of fundamental backing at a quantum mechanics level. Herein, we report on a combined in-situ high energy synchrotron X-ray diffraction and density functional theory (DFT) study to unveil the physical mechanism of phase transformation in a Q&P processed advanced Fe-C steel. Beside a small fraction of bainite, a low air-quenching rate of ∼6 °C/s in the Ms to quenching temperature range leads to carbon enrichment into the untransformed austenite, which simultaneously turns up with the later stage of martensitic transformation at the existing austenite/martensite interfaces. The resulting transformations are ascertained by DFT results and attributed to a second energy barrier in ferrite/martensite facilitation to carbon diffusion to austenite at elevated temperatures, along with the well-known carbon solubility difference/equilibrium in austenite and martensite. The development of cubic martensite in the carbon steel is theoretically elucidated and attributed to more probable hopping sites and diffusion paths of carbon in the ferrite/martensite than in the austenite.

Mesoionic and N-Heterocyclic Carbenes Coordinated N+ Center: Experimental and Computational Analysis.

N-Heterocyclic carbenes, carbocyclic carbenes, remote N-heterocyclic carbenes and N-heterocyclic silylenes are known to form L→N+ coordination bonds. However, mesoionic carbenes (MICs) are not reported to form coordination bonds with cationic nitrogen. Herein, synthesis and quantum chemical studies were performed on 1,2,3-triazol-5-ylidene stabilized N+ center. Six compounds with MIC→N+ ←NHC were synthesized. Density functional theory calculations and energy decomposition analysis were carried out to explore the bonding situation between MIC and N+ center. The C→N+ bond lengths were in the range of 1.295-1.342 Å and bond dissociation energies were <400 kcal/mol. Natural bond orbital analysis supported the presence of excess electron density (>3 electrons) at the N+ center. The computational and X-ray diffraction analysis results confirmed the presence of divalent NI character of center nitrogen and MIC→N+ ←NHC coordination interactions.

Understanding the interaction of organic corrosion inhibitors with copper at the molecular scale: Benzotriazole on Cu(110)

Benzotriazole (BTAH) has been used for several industrial applications, but most commonly as a corrosion inhibitor for copper, since the 1950s. However, the mechanism of its interaction with copper surfaces at the atomistic scale is still a matter of debate. Here, the adsorption of BTAH onto a clean Cu(110) surface has been investigated by a combination of scanning tunnelling microscopy, X-ray photoelectron spectroscopy, high resolution electron energy loss spectroscopy and density functional theory calculations. Different supramolecular structures have been observed depending on molecular coverage and annealing. In the low coverage regime, flat lying deprotonated species are formed which give way to benzotriazolate molecules in an upright configuration by increasing the BTAH exposure. The ensuing monolayer is self-limiting but, upon annealing above 150 °C, transforms into a highly ordered nano-ridge structure resulting from a significant in-plane and out-of-plane reconstruction of the surface. All structures are characterised by a strong molecule–substrate interaction and the high coverage phases are dominated by the formation of metal–organic complexes between copper adatoms and benzotriazolate species. These findings shed light on the nature and strength of the interaction occurring between BTAH and copper which lies at the basis of the effectiveness of this prototypical corrosion inhibitor.

A comparison of three DFT exchange-correlation functionals and two basis sets for the prediction of the conformation distribution of hydrated polyglycine.

The performance of three density functional theory (DFT) exchange-correlation functionals, namely, Perdew-Burke-Ernzerhof (PBE), BP86, and B3LYP, in predicting conformational distributions of a hydrated glycine peptide is tested with two different basis sets in the framework of adaptive force matching (AFM). The conformational distributions yielded the free energy profiles of the DFT functional and basis set combinations. Unlike traditional validations of potential energy and structural parameters, our approach allows the free energy of DFT to be validated. When compared to experimental distributions, the def2-TZVP basis set provides better agreement than a slightly trimmed aug-cc-pVDZ basis set. B3LYP is shown to be better than BP86 and PBE. The glycine model fitted against B3LYP-D3(BJ) with the def2-TZVP basis set is the most accurate and named the AFM2021 model for glycine. The AFM2021 glycine model provides better agreement with experimental J-coupling constants than C36m and ff14SB, although the margin is very small when compared to C36m. Our previously published alanine model is also refitted with the slightly simplified AFM2021 energy expression. This work shows good promise of AFM for developing force fields for a range of proteinogenic peptides using only DFT as reference.