Energy Density Functional Theory Research Articles

Radiation response of Y3Al5O12 and Nd3+-Y3Al5O12 to Swift heavy ions: insight into structural damage and defect dynamics.

Understanding the behavior of a material under irradiation is paramount to its application in the nuclear industry. The present work explores the radiation response of garnet Y3Al5O12 (YAG) and Nd3+-substituted Y3Al5O12 (Nd-YAG) under a 100 MeV Iodine beam at varying fluences to mimic the effect of fission fragments. This is relevant to the potential application of garnet as a host for minor actinide (MA) transmutation (Nd3+: surrogate for long-lived MA (Am3+, Np3+, Cm3+)). The un-irradiated and irradiated YAG and Nd-YAG samples were investigated by X-ray diffraction and Raman spectroscopy. Positron annihilation spectroscopy, thermal spike modelling and theoretical studies have been employed to understand the role of substitution and defect energetics in influencing this radiation response. Although both materials were not completely amorphized under the present irradiation conditions, a tremendous loss in crystallinity could be observed with increase in fluence, the damage being much more in Nd-YAG. Ion track radii of 2.17 nm and 2.91 nm were estimated for YAG and Nd-YAG respectively. Thermal-spike calculations show an increase in radiation-induced transient temperatures upon Nd-substitution that causes greater radiation damage in Nd-YAG. The enhancement in radiation-induced damage with increasing ion-fluence manifests in broadening and weakening of the Raman modes and XRD peaks. An increase in the average positron annihilation lifetime indicated the creation of oxygen vacancies. The defect formation energies of Y3Al5O12 have been theoretically estimated via density functional theory (DFT) and unfavorable energies required for creating cation pair anti-sites have been proposed as one of the possible reasons for the relatively poorer radiation response of YAG. The irradiation behavior of Y3Al5O12 has been compared with disordered fluorite (YSZ) and zirconate pyrochlores, which are well-researched ceramics for MA transmutation.

Direct chemical dynamics simulations of CN- + CH3I bimolecular nucleophilic substitution reaction.

Bimolecular nucleophilic substitution reactions have been studied for more than a century. Experimental and theoretical investigations of these reactions are extensively going on due to their wide applicability and the discovery of new features of these reactions. The CN- + CH3I nucleophilic substitution reaction can result in two isomeric products (NCCH3/CNCH3 + I-) because the incoming nucleophile has two reactive centers. Velocity map imaging experiments of this reaction have been reported and dominant direct rebound dynamics and high internal energy excitation of the reaction products were found in the experiments. However, it was not possible to directly obtain the isomer branching ratios from the experimental data and statistical ratios were predicted based on a numerical simulation. In the present work, direct chemical dynamics simulations of this reaction were performed using density functional theory and semi-empirical potential energy surfaces. Reactivity was low at all collision energies and direct rebound dynamics was observed in a major fraction of trajectories in agreement with experiments. However, the branching ratios computed from the trajectories were different from the previously reported estimates. Product energy distributions and scattering angles were computed and detailed atomic level reaction mechanisms are presented.

Self-Supervised Learning with Atom Replacement for Catalyst Energy Prediction by Graph Neural Networks

Recently, material search utilizing the material informatics (MI) technique is considered to be an efficient approach to discovering new materials. Energy or environment-related materials are deeply related to chemical reactions, and the reactivity of materials is determined by the electronic energy (or internal energy) of materials and reactant systems. The electronic energy is often calculated by first-principles calculation which is based on the Schrödinger equation. Among them, a method called density functional theory (DFT) is widely used nowadays. Since the computational cost of the DFT calculation is relatively high, many graph neural networks (GNNs) for energy prediction have been proposed to obtain the energy values quickly. However, it is difficult to obtain a large amount of labeled data for GNNs training, because a large amount of the DFT data is necessary for training. Although self-supervised learning (SSL) methods using unlabeled data, i.e., no DFT energy information is needed, have been proposed to improve the GNNs’ energy prediction accuracy, existing SSL methods use a non-existent atom of “mask”; this may lead to the inefficient GNNs training. In this work, we propose a mask-less SSL method by replacing some atoms in the target material systems, aiming to improve the energy prediction accuracy of GNNs. Our target systems are catalysts, and here the atoms in unlabeled catalysts are replaced with other actual existing atoms. Then, GNNs predict whether the atoms have been replaced or not for all atoms composing the material. After that, GNNs pre-trained by the proposed SSL are fine-tuned by downstream tasks. We demonstrate the superiority of our proposed method on the Open Catalyst dataset and Poisoned Catalyst dataset using the energy prediction GNNs, CGCNN and PaiNN. The GNNs pre-trained by the proposed SSL achieve the best mean absolute error of predicted energy rather than that of the existing mask-based SSL in all experimental conditions.

Machine Learning Diffusion Monte Carlo Forces

Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C2, H2O, and CH3Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints.

Combined Theoretical Analysis of the Parity-Violating Asymmetry for ^{48}Ca and ^{208}Pb.

The recent experimental determination of the parity violating asymmetry A_{PV} in ^{48}Ca and ^{208}Pb at Jefferson Lab is important for our understanding on how neutrons and protons arrange themselves inside the atomic nucleus. To better understand the impact of these measurements, we present a rigorous theoretical investigation of A_{PV} in ^{48}Ca and ^{208}Pb and assess the associated uncertainties. We complement our study by inspecting the static electric dipole polarizability in these nuclei. The analysis is carried out within nuclear energy density functional theory with quantified input. We conclude that the simultaneous accurate description of A_{PV} in ^{48}Ca and ^{208}Pb cannot be achieved by our models that accommodate a pool of global nuclear properties, such as masses and charge radii, throughout the nuclear chart, and describe-within one standard deviation-the experimental dipole polarizabilities α_{D} in these nuclei.

Ni-doping-induced oxygen vacancy in Pt-CeO2 catalyst for toluene oxidation: Enhanced catalytic activity, water-resistance, and SO2-tolerance

It is a challenge to enhance the catalytic activity of the oxidation of volatile organic compounds (VOCs) and the poison-tolerance capacity in the practical application. Here, we report the construction of Pt/Ni-CeO2 catalyst via Ni doping, which exhibited the excellent toluene catalytic performance, as well as remarkably improved water-resistance and SO2-tolerance. The electron energy loss spectroscopy and density functional theory calculations demonstrated that the doped Ni species induced the generation of abundant oxygen vacancies from bulk to the surface, improving the redox property, activation of oxygen species, and adsorption capacity of toluene molecules. Moreover, the Pt-NiO interfacial structure was formed by the thermal-driven Ni species to the adjacent Pt species, which could modify the electronic and chemical properties of Pt, thus restraining the adsorption of water and SO2 molecules. This investigation provides new insights into the activation of oxygen species via oxygen vacancies, and anti-poison activity via surface modification engineering for catalyst development in practical applications.

Influence of intercalated Gd atoms on graphene-4H-SiC(0001) properties

In this work, the influence of intercalated gadolinium (Gd) atoms on graphene-4H-SiC(0001) properties are presented, using X-ray photoelectron spectroscopy, low energy electron diffraction and density functional theory calculations. Detailed analysis, ranging from adsorption to temperature induced intercalation under the graphene, of the chemical composition, surface structures, charge transfer and changes in the graphene band structure is performed. The investigation is carried out for various initial Gd film thicknesses and temperature of annealing, which allows to obtain and observe interactions between elements in the subsequent stages of intercalation. Calculations show that the deeper Gd is placed into the substrate the more energetically favourable configuration is obtained, which indicates the possibility of intercalation process to occur. Moreover, intercalated Gd strongly affects the electronic properties of both graphene and buffer layer. Simultaneously, experiment results show that Gd induces n-type doping in graphene. Additionally, temperature induced intercalation causes the buffer layer decoupling and transformation into new graphene one. The presence of gadolinium at the graphene-SiC interface results also in chemical shifts to the lower binding energy range and follows the trend in which the more gadolinium intercalates the higher shift is observed.

Quinary, Senary, and Septenary High Entropy Alloy Nanoparticle Catalysts from Core@Shell Nanoparticles and the Significance of Intraparticle Heterogeneity.

Colloidally prepared core@shell nanoparticles (NPs) were converted to monodisperse high entropy alloy (HEA) NPs by annealing, including quinary, senary, and septenary phases comprised of PdCuPtNi with Co, Ir, Rh, Fe, and/or Ru. Intraparticle heterogeneity, i.e., subdomains within individual NPs with different metal distributions, was observed for NPs containing Ir and Ru, with the phase stabilities of the HEAs studied by atomistic simulations. The quinary HEA NPs were found to be durable catalysts for the oxygen reduction reaction, with all but the PdCuPtNiIr NPs presenting better activities than commercial Pt. Density functional theory (DFT) calculations for PdCuPtNiCo and PdCuPtNiIr surfaces (the two extremes in performance) found agreement with experiment by weighting the adsorption energy contributions by the probabilities of each active site based on their DFT energies. This finding highlights how intraparticle heterogeneity, which we show is likely overlooked in many systems due to analytical limitations, can be leveraged toward efficient catalysis.

Degradation mechanism of BPA under VUV irradiation: efficiency contribution and DFT calculations.

Bisphenol A (BPA) is regarded as a hazardous pollutant that exists widely in aquatic environments, posing a severe threat to human health. In this study, a vacuum ultraviolet (VUV) lamp emitting a hybrid of 254nm and 185nm light was used to degrade BPA. Results indicated that photolysis via 254nm wavelength accounted for 24.93% for BPA decay, while indirect oxidation was responsible for 52.27% of decay. Results confirmed that the degradation of BPA under VUV illumination mainly occurred via photo-excited degradation and ·OH electrophilic addition reactions based on average local ionization energy (ALIE) calculation and density functional theory (DFT) calculations. Therefore, only light with a wavelength of 254nm was able to induce the first three excited states of BPA, forming the electron transition type of n → π* from O atom to a single benzene ring and π → π* in the single benzene ring. Indirect oxidation by ·OH occurred as it preferentially attacked the C6 atom in BPA ring A. Moreover, the energy required for photo-excited degradation was about twofold than that of ·OH electrophilic addition reactions.

Mechanochemical bottom-up synthesis of phosphorus-linked, heptazine-based carbon nitrides using sodium phosphide.

Herein, we present the bottom-up, mechanochemical synthesis of phosphorus-bridged heptazine-based carbon nitrides (g-h-PCN). The structure of these materials was determined through a combination of powder X-ray diffraction (PXRD), X-ray photoelectron spectroscopy (XPS), 31P magic angle spinning nuclear magnetic resonance (MAS NMR), density functional theory (DFT) and electron energy loss spectroscopy (EELS). Compared to traditional furnace-based techniques, the presented method utilizes milder conditions, as well as shorter reaction times. Both samples of g-h-PCN directly after milling and aging and after an hour of annealing at 300 °C (g-h-PCN300) show a reduction in photoluminescent recombination, as well as a nearly two-time increase in photocurrent under broad spectrum irradiation, which are appealing properties for photocatalysis.

Density gradient segregation of Cu at the Si2Hf/Al interface in an Al-Si-Cu-Hf alloy

Solute segregation at the Si2Hf/Al interface of an Al-Si-Cu-Hf alloy was investigated by combining high angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), energy dispersive X-ray spectroscopy (EDS) and density functional theory (DFT) calculation. Density gradient segregation of Cu atoms was observed at the habit plane of the nanobelt-like Si2Hf phase, with the Cu concentrations gradually increasing to the Si2Hf/Al interface. This gradient segregation of Cu is found to be beneficial for decreasing both the strain and interfacial energies and thereby promoting the stabilization of the Si2Hf precipitates during heat treatment. This study provides an insight into the enhanced stability of Si2Hf precipitates in Al-Si-Cu-Hf alloy.

Accreting neutron stars from the nuclear energy-density functional theory

Context. The accretion of matter onto the surface of a neutron star in a low-mass X-ray binary triggers X-ray bursts, whose ashes are buried and further processed thus altering the composition and the properties of the stellar crust. Aims. In this second paper of a series, the impact of accretion on the equation of state and on the global properties of neutron stars is studied in the framework of the nuclear energy-density functional theory. Methods. Considering ashes made of 56Fe, we calculated the equations of state using the same Brussels-Montreal nuclear energy-density functionals BSk19, BSk20, and BSk21, as those already employed for determining the crustal heating in our previous study for the same ashes. All regions of accreting neutron stars were treated in a unified and thermodynamically consistent way. With these equations of state, we determined the mass, radius, moment of inertia, and tidal deformability of accreted neutron stars and compared with catalyzed neutron stars for which unified equations of state based on the same functionals are available. Results. The equation of state of accreted neutron stars is found to be significantly stiffer than that of catalyzed matter, with an adiabatic index Γ ≈ 4/3 throughout the crust. For this reason, accreting neutron stars have larger radii. However, their crustal moment of inertia and their tidal deformability are hardly changed provided density discontinuities at the interface between adjacent crustal layers are properly taken into account. Conclusions. The enhancement of the stiffness of the equation of state of accreting neutron stars is mainly a consequence of nuclear shell effects, thus confirming the importance of a quantum treatment as stressed in our first study. With our previous calculations of crustal heating using the same functionals, we have thus obtained consistent microscopic inputs for simulations of accreting neutron stars.

Adsorption of the prototypical organic corrosion inhibitor benzotriazole on the Cu(100) surface

The interaction of benzotriazole (BTAH) with Cu(100) has been studied as a function of BTAH exposure in a joint experimental and theoretical effort. Scanning tunnelling microscopy (STM), X-ray photoelectron spectroscopy (XPS), high resolution electron energy loss spectroscopy (HREELS) and density functional theory (DFT) calculations have been combined to elucidate the structural and chemical characteristics of this system. BTAH is found to deprotonate upon adsorption on the copper surface and to adopt an orientation that depends on the molecular coverage. Benzotriazolate (BTA) species initially lie with their planes parallel to the substrate but, at a higher molecular coverage, a transition occurs to an upright adsorption geometry. Upon increasing the BTAH exposure, different phases of vertically aligned BTAs are observed with increasing molecular densities until a final, self-limiting monolayer is developed. Both theory and experiment agree in identifying CuBTA and Cu(BTA)2 metal-organic complexes as the fundamental building blocks of this monolayer. This work shows several similarities with the results of previous studies on the interaction of benzotriazole with other low Miller index copper surfaces, thereby ideally completing and concluding them. The overall emerging picture constitutes an important starting point for understanding the mechanism for protection of copper from corrosion.

Homogeneous ice nucleation in an ab initio machine-learning model of water

Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.

Phase Transformation-Induced Quantum Dot States on the Bi/Si(111) Surface.

Nanopatterns at near atomic dimensions with controllable quantum dot states (QDSs) are promising candidates for the continued downscaling of electronic devices. Herein, we report a phase transition-induced QD system achieved on the √3 × √3-Bi/Si(111) surface reconstruction, which points the way to a novel strategy on QDS implementation. Combining scanning tunneling microscopy, scanning tunneling spectroscopy, and density functional theory (DFT) calculations, the structure, energy dispersion, and size effect on band gap of the QDs are measured and verified. As-created QDs can be manipulated with a dot size down to 2 nm via Bi phase transformation, which, in turn, is triggered by thermal annealing at 700 K. The transition mechanism is also supported by our DFT calculations, and an empirical analytical model is developed to predict the transformation kinetics.

Effect of functional groups on tribological properties of lubricants and mechanism investigation

Nine organic compounds were utilized as model lubricants to investigate the impact of functional groups on tribological performances. Nonanoic Acid with carboxyl showed the best lubrication properties, and fluid film and tribofilm were coexistent in its friction test, bringing a low friction coefficient and wear rate. In addition, the lubricant with low friction coefficient corresponded to high adsorption energy in density functional theory (DFT) calculations. And the lubricant forming adsorption film with large surface energy displayed small wear rate in friction test. Moreover, adsorption energies positively correlated surface energies. Based on the experimental results, the action mechanism of functional groups on tribological properties of lubricants was proposed. Various functional groups make lubricant molecules show different adsorption energies and surface energies. Lubricant molecules with high adsorption energy are more likely to adsorb on substrates and form a vertical monolayer, which can maintain a regular molecular brush structure during friction and bring a low friction coefficient. And lubricant molecules with high surface energy may be more prone having tribochemical reactions during friction and forming protective tribofilm, which leads to a low wear rate.

Bandgap Funneling in Bismuth-Based Hybrid Perovskite Photocatalyst with Efficient Visible-Light-Driven Hydrogen Evolution.

The photocatalytic system using hydrohalic acid (HX) for hydrogen production is a promising strategy to generate clean and renewable fuels as well as value-added chemicals (such as X2 /X3 - ). However, it is still challenging to develop a visible-light active and strong-acid resistive photocatalyst. Hybrid perovskites have been recognized as a potential photocatalyst for photovoltaic HX splitting. Herein, a novel environmentally friendly mixed halide perovskite MA3 Bi2 Cl9-x Ix with a bandgap funnel structure is developed, i.e., confirmed by energy dispersive X-ray analysis and density functional theory calculations. Due to gradient neutral formation energy within iodine-doped MA3 Bi2 Cl9 , the concentration of iodide element decreases from the surface to the interior across the MA3 Bi2 Cl9-x Ix perovskite. Because of the aligned energy levels of iodide/chloride-mixed MA3 Bi2 Cl9-x Ix , a graded bandgap funnel structure is therefore formed, leading to the promotion of photoinduced charge transfer from the interior to the surface for efficient photocatalytic redox reaction. As a result, the hydrogen generation rate of the optimized MA3 Bi2 Cl9-x Ix is enhanced up to ≈341±61.7µmolh-1 with a Pt co-catalyst under visible light irradiation.

A simple lattice projection method for estimating conformer strain energies using diamond or Lonsdaleite lattices to identify atomistic steric interactions and dihedral strain

A simple lattice projection method for estimating conformer strain energies using diamond or Lonsdaleite lattices to identify atomistic steric interactions and dihedral strain

2D boron nitride material as a sensor for H2SiCl2

The utilization of 2D materials and nanostructured composites as potential sensors and adsorbent surface for various molecular processes has increased tremendously with the upsurge in the use of sensors for various applications. In this work, the utilization of boron nitride nanosheet (BNNS) and its aluminium and gallium (BNAlNS and BNGaNS) doped derivatives as sensors for dichlorosilane (C3H7NO3) have been studied computationally by employing the B3LYP-D3 model with the 6-311G(d) basis set as well as the PBE0, ωB97XD, and M06-2X functionals. The adsorption and sensing potential of these nanosheets was tested against the highly toxic and flammable dichlorosilane (C3H7NO3) gas molecule. Adsorption interactions of the gas with the nanosheets was computed and reported. To understand the type of intermolecular interactions occurring during the adsorption process, quantum theory of atoms in molecules (QTAIM) and natural bond orbital analysis (NBO) for the estimation of bond order was computed and reported. The reactivity and sensing attributes of the studied nanosheets were further appraised by the conceptual density functional theory (CDFT) and HOMO-LUMO energy gap. The computed energy gap shows a decreasing trend upon adsorption of DCS gas, the obtained values for the nanostructures were as follows: BNNS: 5.729, BNAlNS: 5.739, and BNGaNS: 5.742 eV. The results of the adsorption interaction divulged that the gallium doped nanosheet (BNGaNS) exhibited higher adsorption properties and sensitivity towards DCS gas when compared with other studied derivatives.

16O + 16O cluster states and their fusion to 32S: A non-monotonic potential description

Non-monotonic (NM) nucleus-nucleus potential families rooted in the Pauli-compliant Energy Density Functional (EDF) theory and especially their behavior at higher energies have been investigated using experimental data for 16O + 16O elastic scattering in the range . At , the 16O + 16O NM potentials in a simple optical model (OM) are found to support five rotational bands of 16O + 16O bound and quasi-molecular states in 32S with the cluster global quantum numbers G = 24, 26, 28, 30 and 32. The lowest band in the experimental spectrum starting at , and having , 2+, 4+, 6+ and 8+, has been located for the first time. In the simple OM, the NM potentials are found to reproduce the experimental 16O + 16O fusion cross-sections in the energy range 12–31 MeV. The present work shows that both the 16O + 16O cluster bands and fusion cross-sections in the Coulomb barrier region can be reproduced using NM potentials.