Modeling palladium surfaces with density functional theory, neural networks and molecular dynamics

In silico property prediction based on density functional theory (DFT) is increasingly performed for crystalline materials. Whether quantitative agreement with experiment can be achieved with current methods is often an unresolved question, and may require detailed examination of physical effects such as electron correlation, reciprocal space sampling, phonon anharmonicity, and nuclear quantum effects (NQE), among others. In this work, we attempt first-principles equation of state prediction for the crystalline materials ScF3 and CaZrF6, which are known to exhibit negative thermal expansion (NTE) over a broad temperature range. We develop neural network (NN) potentials for both ScF3 and CaZrF6 trained to extensive DFT data, and conduct direct molecular dynamics prediction of the equation(s) of state over a broad temperature/pressure range. The NN potentials serve as surrogates of the DFT Hamiltonian with enhanced computational efficiency allowing for simulations with larger supercells and inclusion of NQE utilizing path integral approaches. The conclusion of the study is mixed: while some equation of state behavior is predicted in semiquantitative agreement with experiment, the pressure-induced softening phenomenon observed for ScF3 is not captured in our simulations. We show that NQE have a moderate effect on NTE at low temperature but does not significantly contribute to equation of state predictions at increasing temperature. Overall, while the NN potentials are valuable for property prediction of these NTE (and related) materials, we infer that a higher level of electron correlation, beyond the generalized gradient approximation density functional employed here, is necessary for achieving quantitative agreement with experiment.

Density Functional Theory (DFT) calculation at B3LYP level of theory and 6-31G* basis set was applied on some triazole derivatives of pyrimidine which led to the optimization of their structures, generation of electronic and other important Quantum chemical descriptors such as the energy of the highest occupied molecular orbital (EHOMO), the energy of the lowest unoccupied molecular orbital (ELUMO), energy band gap (ΔE), Dipole Moment (μ), chemical hardness (η), chemical softness (σ), global electronegativity() and number of transferred electrons () using SPARTAN’14 Software. The obtained results shows a good correlation between the chemical structures of the inhibitors and their experimental inhibition efficiencies (%IEs). The ranking of these efficiencies (%IEs) nicely matched with the order of a good number of the generated descriptors but with a varying degree of correlation as majority of the descriptors indicates that I-4 is the best inhibitor among the data set. Furthermore, molecular dynamic (MD) simulations were carried out to search the best adsorption configuration of the inhibitor on the steel (1 1 0) surface using Material Studio 8.0. The obtained results of MD simulations suggest that the interaction was as a results of the chemical adsorption on the steel surface, since the binding energy > 100 Kcalmol-1 for all the inhibitors and the best adsorption energy was found to be -488.07 Kcalmol-1 (I-4). This observation are in good agreement with the DFT results and the experiment findings. Thus; this study provides a valuable approach and new direction to novel steel corrosion inhibitor discovery.

Transition metal dichalcogenides are promising materials for high-performance electronics, whereas the impact of defects on their electronic properties remains elusive. Here, we employ neural network potentials (NNPs) constructed from density functional theory (DFT) data to investigate defect-laden WS2 thin films. Molecular dynamics simulations reveal that at low defect concentrations (S/W ratio of 1.9), single sulfur vacancies are predominant. Conversely, at high defect concentrations (S/W ratio of 1.7), complex defects with short lifetimes appear. Additionally, DFT results indicate that the band gap persists at S/W = 1.9 but disappears at 1.7, aligning with observed device degradation at high defect concentrations.

Density functional theory (DFT) molecular dynamics (MD) and classical MD simulations of the principal shock Hugoniot are presented for two hydrocarbon polymers, polyethylene (PE) and poly(4-methyl-1-pentene) (PMP). DFT results are in excellent agreement with experimental data, which is currently available up to 80 GPa. Further, we predict the PE and PMP Hugoniots up to 350 and 200 GPa, respectively. For comparison, we studied two reactive and two nonreactive interaction potentials. For the latter, the exp-6 interaction of Borodin et al. showed much better agreement with experiment than OPLS. For the reactive force fields, ReaxFF displayed decidedly better agreement than AIREBO. For shocks above 50 GPa, only the DFT results are of high fidelity, establishing DFT as a reliable method for shocked macromolecular systems.

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.

HighlightsAccording to the design of the experiment (DoE), multivariate analysis models were used to optimize the critical process parameters combined with multi-objective optimization.Based on the optimized operating conditions, the MILT-HD method not only enhances the extraction efficiency from Amomi fructus but also reduces energy demands and CO2 emissions.Based on the density functional theoretical (DFT) and molecular dynamics (MD) simulations, the mechanisms for ionic liquids (ILs) to improve the extraction efficiency of essential oil was comprehensively revealed.In this paper, Amomi fructus (Latin) was used to explore the mechanism of ionic liquids (ILs) in improving the extraction efficiency of essential oils. Microwave assisted ionic liquid treatment followed by a hydro-distillation (MILT-HD) process for isolating Amomi fructus essential oil was optimized by multi-objective optimization. Under optimum operating conditions, the IL-assisted extraction method not only enhances extraction efficiency but also reduces energy demands and CO2 emissions. Since the hydrogen bond structure network of cellulose in the cell wall is an important reason for hindering diffusion of essential oils, the mechanism of ILs was explored by density functional theoretical (DFT) and molecular dynamics (MD) simulations. According to DFT calculations, ILs can facilitate the cleavage of cellulose chains and have strong non-covalent interactions with cellulose. Based on the MD simulations, the degree of destruction of the cellulose hydrogen bond structure was explored. According to the DFT and MD simulations, the ILs can significantly destroy cellulose structure, thereby promoting essential oil release from the plant. These results were confirmed by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). This work is conducive to better understand the MILT-HD process for isolating essential oil and comprehensively understand the mechanism of ILs in the extraction process.

This study investigates the structure and evolution of lanthanum acetate hydrated clusters using density functional theory (DFT) and molecular dynamics (MD) simulations to explore the influence of organic ammonium leaching...

In recent years, various environmental problems such as rising temperature, droughts, and extreme weather events have become more serious in many parts of the world because of global warming. One of the causes of the global warming is an increase in emissions of greenhouse gases such as CO2, CH4, and N2O, those are emitted during a combustion of fossil fuels such as coal, oil, and natural gas in thermal power generation. As energy consumption accelerates year by year, the depletion of fossil fuels is also becoming an issue. Therefore, hydrogen energy is attracting attention. Hydrogen is known as a clean energy because it reacts with oxygen to produce only water and chemical energy.Fuel cells are attracting attention as a new power generation device that produces electricity from hydrogen energy. Fuel cells generate electricity directly through chemical reactions, resulting in minimal power generation loss from energy conversion. Solid oxide fuel cells (SOFC), in which solid oxide electrolyte membrane (SOEM) is used as the electrolyte membrane, have particularly high-power generation efficiency. On the other hand, the operating temperature of SOFCs is extremely high (over 1000 K), which accelerates degradation of SOEM. Therefore, it is necessary to lower the operating temperature from high to medium (about 700 K) while maintaining high power generation efficiency.To solve this problem, we focus on dual-phasing and nano-structuring of SOEMs. Previous studies of SOEM have shown that dual-phase membranes have better ion transport performance than a single-phase membrane. For example, a dual-phase SOEM is composed of SSC (SrSc0.1Co0.9O3-d) with perovskite structure and SDC (Sm0.2Ce0.8O2-d) with fluorite structure. One the other hand, nano-structuring are nano-thin film and nano-crystalline. Nano-thin film is a method of forming an electrolyte film of several μm by layering nano-films of several nm in size. Nano-crystalline is a method to reduce a crystal size to several tens of nanometers. By these nano structures, the resistance of ion conduction is reduced, and ion conductivity is improved.However, the number of interfaces in dual-phase SOEM and nano structured membrane is expected to increase. Previous studies have reported a decrease in ion conductivity near the interface between different structures of membrane. This decrease in ion conductivity at the interface is caused by an increase in the energy barrier as the conduction path passes through the interface. The correlation between the reduction of ion conductivity and nano structures in membrane has not been clarified clearly.In this study, we analyze the mechanism of oxygen ion conductivity reduction near the interface by numerical simulations. Molecular simulations are performed to build up information by multi-scale analysis. Specifically, density functional theory (DFT), molecular dynamics (MD), and kinetic Monte Carlo (kMC) are performed in a bottom-up manner. First, MD simulations were performed using a potential model constructed from the energy states in the atomic arrangement at the interface obtained by DFT. We analyzed the ion conductivity at the interface and its crystal size dependence. We also create a hopping model of oxygen ions moving from site to site for kMC simulations. Next, kMC simulations are performed to analyze the oxygen ion conductivity in the dual-phase SOEM including interface, on a time and spatial scale similar to that of a real SOEMs.In this presentation, we report on the results of MD simulation. A dual-phase SOEM model consisting of SSC and SDC was used as the simulation model (Fig.1(a)). The diffusion coefficient of oxygen ions was obtained as an indicator of ionic conductivity. Because the ionic conductivity is determined by the balance between the energy barrier of the conduction path and the kinetic energy of the ions involved in ionic conduction. In this study, the activation energy for the diffusion was calculated as the energy barrier. The activation energy was estimated from Arrhenius relation of diffusion coefficient. The number of unit cells in the dual-phase SOEM model was varied to change the crystal size. As a result, both the diffusion coefficient and activation energy increase as the crystal size of SSC increases. On the other hand, as the crystal size of SDC increases, although the diffusion coefficient decreases, the activation energy increases (Fig.1(b)). These results show the dependence of the diffusion coefficient and activation energy on the crystal size in a dual-phase SOEM. Figure 1

Phonon thermal transport is a key feature for the operation of thermoelectric materials, but it is challenging to accurately calculate the thermal conductivity of materials with strong anharmonicity or large cells. In this work, a deep neural network potential (NNP) is developed using a dataset based on density functional theory (DFT) and applied to describe the lattice dynamics of Sb2Te3 and Bi2Te3/Sb2Te3 superlattices. The lattice thermal conductivities of Sb2Te3 are first predicted using equilibrium molecular dynamics (EMD) simulations combined with an NNP and the results match well with experimental values. Then, through further exploration of weighted phase spaces and the Grüneisen parameter, we find that there is a stronger anharmonicity in the out-of-plane direction in Sb2Te3, which is the reason why the thermal conductivities are overestimated more in the out-of-plane direction than in the in-plane direction by solving the phonon Boltzmann transport equation (BTE) with only three-phonon scattering processes being considered. More importantly, the lattice thermal conductivities of Bi2Te3/Sb2Te3 superlattices with different periods are accurately predicted using non-equilibrium molecular dynamics (NEMD) simulations together with an NNP, which serves as a good example to explore the thermal transport physics of superlattices using a deep neural network potential.

The structural, mechanical, and electronic properties of perovskite molybdates are a topic of frequent study in materials science. In this study, the influence of Mg doping on the physical metallurgy of perovskite molybdates is investigated using first-principles calculations based on density functional theory (DFT) and molecular dynamics (MD) simulation. Our calculated optimized lattice parameters (3.9945, 3.8964, 3.8634, 3.8440, and 3.7952 Å [mentioned only DFT data, MD data listed in Table 2] for x = 0, 0.1, 0.2, 0.3, and 0.4 respectively) of SrMo1−xMgxO3 are highly consistent with other experimental results (3.9762, 3.9695, and 3.9649 Å for x = 0, 0.1, and 0.2 respectively) and some available theoretical results (3.9720 Å for x = 0, no previous data available for Mg-doped systems). The calculated elastic constants satisfied the Born stability criteria, indicating that our studied materials are mechanically stable at all doping concentrations, which was also confirmed by the calculated negative values of Cohesive energy. The mechanical behaviors of perovskite, including elastic constants, elastic moduli, ductility, and elastic anisotropy, were investigated and discussed. Our computed results suggest that Mg doping can increase elastic moduli. The calculated Pugh’s ratio increased from 0.42 to 0.71 [DFT] and from 0.47 to 0.75 [MD] as well as the Poisson’s ratio decreased from 0.31 to 0.21 [DFT] and from 0.30 to 0.21 [MD] which transformed the compound from ductile to brittle due to the addition of Mg at Mo-site. The band structures, density of states, and charge density redistributions of the undoped and Mg-doped materials were predicted. Our simulation outputs clearly illustrated the importance of accounting for Mg doping’s influence in theoretical simulations of the physical properties of the presently studied perovskite material.

We present an overview of the current status of experimental, theoretical, molecular dynamics (MD), and density functional theory (DFT) studies of argon vapor-to-liquid nucleation. Since the experimental temperature-supersaturation domain does not overlap with the corresponding MD and DFT domains, separate comparisons have been made: theory versus experiment and theory versus MD and DFT. Three general theoretical models are discussed: Classical nucleation theory (CNT), mean-field kinetic nucleation theory (MKNT), and extended modified liquid drop model-dynamical nucleation theory (EMLD-DNT). The comparisons are carried out for the area below the MKNT pseudospinodal line. The agreement for the nucleation rate between the nonclassical models and the MD simulations is very good--within 1-2 orders of magnitude--while the CNT deviates from simulations by about 3-5 orders of magnitude. Perfect agreement is demonstrated between DFT results and predictions of MKNT (within one order of magnitude), whereas CNT and EMLD-DNT show approximately the same deviation of about 3-5 orders of magnitude. At the same time the agreement between all theoretical models and experiment remains poor--4-8 orders of magnitude for MKNT, 12-14 orders for EMLD-DNT, and up to 26 orders for CNT. We discuss possible reasons for this discrepancy and the ways to carry out experiment and simulations within the common temperature-supersaturation domain in order to produce a unified picture of argon nucleation.

Fractionation of alkylated carbazoles in petroleum during subsurface migration: Evidence from molecular simulation and application in sandstone reservoirs

In a recent study [Science, 2015, 347, 6224], protein engineering was used to design a core within the enzyme threonyl-tRNA synthetase (ThrRS) capable of stabilizing the coplanar transition state conformation of an inserted noncanonical p-biphenylalanine (BiPhe) residue. Using the X-ray crystal structures of the preliminary (Protein Data Bank entries 4S02, 4S0J, 4S0L, 4S0I, and 4S0K) and final (PDB entry 4S03) ThrRS proteins, fully quantum mechanical (QM) cluster models were constructed and analyzed. Density functional theory and molecular dynamics computations were performed to investigate the energetic profiles of BiPhe dihedral rotation within the ThrRS models. For the 4S03 model, results indicate that steric and hydrophobic forces of the residues surrounding BiPhe eliminate the coplanar transition state entirely. Molecular dynamics simulations were carried out that confirmed the extent of BiPhe rotational flexibility, and provided additional information on barrier heights of full BiPhe rotation. Transition states of near-coplanar biphenyl rings of BiPhe were found for the 4S0I and 4S0K models, but are not likely persistent on any observable timescale. The dihedral angle of the biphenyl moiety is thermally allowed to fluctuate within the ThrRS protein core models by a range of 17°-26°. BiPhe-residue interaction counts (RICs) were used to compare the interaction differences among the different ThrRS cores. The RICs demonstrate how BiPhe is compacted within the 4S03 core, resulting in the experimentally observed "trapped" coplanar transition state analogue. This work presents a unique application of QM-cluster models towards studying the inner workings of proteins, and suggests avenues that computational chemistry can be used to further guide bioengineering.

Clay minerals, which are prevalent gangue minerals, are found in tailings and beneficiation effluent after the extraction of valuable minerals. The surface of clay mineral particles is easy to hydrate, which makes it the main factor restricting tailings separation and wastewater treatment. However, the microscopic mechanism of clay mineral particle surface hydration is not yet systematic. In recent years, with the development of molecular simulation theory and the improvement of computational efficiency, density functional theory (DFT) and molecular dynamics (MD) have gradually become a powerful tool for studying the surface hydration of clay mineral particles, which provides new insight into the interaction between the crystal structures of clay minerals and the interfacial interaction in surface hydration of clay mineral particles at the molecular or atomic levels. This article first reviews the basic theory of DFT and MD, then reviews the research progress on clay mineral surface hydration. From the perspective of molecular simulation, a comprehensive discussion of the clay mineral phase structure, the establishment of the supercell surface model, the clay-water interface interaction and the limitations of molecular simulation was conducted. Water molecules can adsorb with different mineral surfaces in slime water through hydrogen bond, which is the basis of surface hydration mechanism. The hydration layer is composed of three water layers with different densities, with a thickness of about 8-10 Å. Water and ions form hydrate cations, which are adsorbed on the surface of clay minerals, change the structure of water layer on the surface of minerals. This article ends with a brief discussion of conclusions and perspectives.

Modelling ultrafast dynamics at a conical intersection with regularized diabatic states: An approach based on multiplicative neural networks