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Machine learning potentials (MLPs) are promising for various chemical systems, but their complexity and lack of physical interpretability challenge their broad applicability. This study evaluates the transferability of the deep potential (DP) and neural equivariant interatomic potential (NequIP) models for graphene–water systems using numerical metrics and physical characteristics. We found that the data quality from density functional theory calculations significantly influences MLP predictive accuracy. Prediction errors in transferring systems reveal the particularities of quantum chemical calculations on the heterogeneous graphene–water systems. Even for supercells with non-planar graphene carbon atoms, k-point mesh is necessary to obtain accurate results. In contrast, gamma-point calculations are sufficiently accurate for water molecules. In addition, we performed molecular dynamics (MD) simulations using these two models and compared the physical features such as atomic density profiles, radial distribution functions, and self-diffusion coefficients. It was found that although the NequIP model has higher accuracy than the DP model, the differences in the above physical features between them were not significant. Considering the stochasticity and complexity inherent in simulations, as well as the statistical averaging of physical characteristics, this motivates us to explore the meaning of accurately predicting atomic force in aligning the physical characteristics evolved by MD simulations with the actual physical features. [ABSTRACT FROM AUTHOR]

The water/ethanol system possesses complexities at the molecular level, which render its description a difficult task. For the elucidation of the system's hydrogen bonding features that are the key factors in its complex behavior, we conduct a Density Functional Theory analysis on relevant water/ethanol clusters inside implicit solvent cavities for the determination of the ethanol donor hydrogen bond strength. We record Attenuated Total Reflectance spectra of water/ethanol-OD solutions and utilize our density and refractive index measurements for post-processing. The application of the Badger–Bauer rule reveals a minimum in the strength of the ethanol donor hydrogen bond for a composition of xwater = 0.74. We attempt to analyze further this result by estimating the effect of the implicit solvent on the ethanol donor hydrogen bond strength, finding it to be incremental. A brief analysis of different cluster conformations is carried out to determine the cooperativity conditions that can potentially explain the observed minimum in the ethanol donor hydrogen bond strength. These observations are related to notions of microheterogeneity in water/alcohol mixtures and provide context toward a more elaborate picture of association in heteroclusters. [ABSTRACT FROM AUTHOR]

The shock response of fully-dense and porous crystalline tellurium dioxide (TeO 2) to the high-pressure and high-temperature fluid regime was investigated within the framework of density functional theory with Mermin's generalization to finite temperatures. The principal and porous shock Hugoniot curves were predicted from canonical ab initio molecular dynamics (AIMD) simulations, with the phase space sampled along isotherms up to 80 000 K, for densities ranging from ρ = 3 to 17 g/cm 3. The polymorphs investigated are α - Te O 2 paratellurite (P 4 1 2 1 2), Te O 2 cotunnite (P n m a), and Te O 2 post-cotunnite (P 2 1 / m). Based on the discontinuity found in the calculated U s − u p slope of TeO 2 post-cotunnite at a shock velocity of U s ≃ 8.35 km/s and a particle velocity of u p ≃ 3.64 km/s, the shock melting temperature and pressure are predicted to be ≃ 6500 K and ≃ 170 GPa. Results from the AIMD simulations are in line with the static compression data of Te O 2 paratellurite and cotunnite, and with the recent shock Hugoniot data for single-crystal α - Te O 2 for pressures up to 85 GPa, obtained using the inclined-mirror method and the velocity interferometer system for any reflector combined with powder gun and two-stage light-gas gun. [ABSTRACT FROM AUTHOR]

This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all- d -metal Heusler alloys. The model was trained using descriptors related to the mean properties of individual atoms, the properties of simple compounds in their ground state, and measures of chemical disorder. It demonstrated high accuracy in predicting structural properties, while exhibiting moderate accuracy in predicting magnetic properties. To identify optimal alloy compositions, a genetic algorithm was used to find those with the greatest differences in magnetization during martensitic transitions. Using this combined approach, the Ni–Co–Mn–Ti alloy system was thoroughly explored, resulting in the discovery of an alloy with a maximum magnetization difference. These results are consistent with previous research based on density functional theory and highlight the effectiveness of integrating machine learning with genetic algorithms for the discovery of new materials with outstanding magnetocaloric properties. The study emphasizes the need for further refinement of models capable of accurately predicting complex magnetic interactions, which is essential for fully leveraging the potential of all- d -metal Heusler alloys in practical applications. [ABSTRACT FROM AUTHOR]

Bi2MoO6 nanospheres with surface oxygen vacancies (SOVs) controlled by the calcination process were prepared in this study. Performance testing revealed that the Bi2MoO6-4 sample (Bi2MoO6 calcined at 350 °C for 4 h) with SOVs achieved a remarkable photocatalytic degradation efficiency up to 99.16% for Rhodamine B (RhB) within 50 min, which is 2.19 times higher than that of pure Bi2MoO6. The higher photocatalytic performance of the Bi2MoO6-4 sample is attributed to the SOVs' defect level located at the Bi2MoO6 bandgap, narrowing the bandgap to effectively promote the photogenerated charge separation. The promotion of photocarrier separation and electron were transferred due to the Bi–O bond breakage in the Bi2MoO6-4 [Bi2O2]2+ layer, which mediates the defect level of SOVs in the band structure. The density functional theory calculation results reveal the possible formation site of the oxygen vacancy and the vacancy-induced defect states. This study provides a new approach for fabricating new photocatalysts with surface oxygen defects. [ABSTRACT FROM AUTHOR]

Molybdenum disulfide (MoS2) is a promising electrocatalyst for the hydrogen evolution reaction (HER) due to excellent stability and low cost. However, the utilization in electrocatalytic hydrogen evolution is constrained by inherent shortcomings, including fewer edge active sites, poor dispersion, and electrical conductivity. In this work, MoS2 was compounded with carbon nanotubes (CNTs), which are known for their high specific surface area and excellent electrical conductivity. These CNTs, laden with oxygen-containing functional groups, provided nucleation sites that facilitated the rapid assembly of MoS2 nanoflowers under hydrothermal conditions within 3 h. Due to their diminutive size (∼300 nm), these nanoflowers possess a large specific surface area and numerous active sites at their edges. Furthermore, MoS2 nanoflowers exhibited a high concentration of intrinsic S-vacancies. This heterojunction material exhibited superior HER properties. In addition, density functional theory simulation further confirmed that the MoS2 with S vacancy and CNT heterojunction electrocatalysts (VS-M/C) provided a fast charge transfer pathway for water electrolysis, and analysis showed that the conduction band minimum and valence band maximum were mainly contributed by the d orbits of Mo and the p orbits of C. This study proffered a novel approach for the engineering of high-performance MoS2-based HER electrocatalysts. [ABSTRACT FROM AUTHOR]

This study employs high-level quantum chemical calculations to determine the global minimum structure of Au10 clusters definitively. Contrary to previous reports, coupled-cluster singles and doubles with perturbative triples [CCSD(T)] calculations with sizable quadruple-ζ basis sets incorporating the spin–orbit (SO) effect reveal that the planar 10.b structure is the true global minimum for Au10, not the three-dimensional 10.a structure. Two-component spin–orbit density functional theory calculations demonstrate that the SO effect is minimal for most Au10 isomers, except for the 10.f structure. A straightforward diagnostic tool is proposed for identifying Au cluster structures with strong spin–orbit coupling based on 6p orbital occupation. The calculated IR spectra based on Boltzmann averaging the six Au10 isomers show good agreement with recent experimental spectra although minor discrepancies are noted potentially due to interactions with Kr. The results suggest that the transition point to non-planar global minimum structures for Au clusters lies beyond Au10 but is nearby. [ABSTRACT FROM AUTHOR]

We introduce a methodological framework coupling machine-learning potentials, ring polymer molecular dynamics (RPMD), and kinetic Monte Carlo (kMC) to draw a comprehensive physical picture of the collective diffusion of hydrogen atoms on metal surfaces. For the benchmark case of hydrogen diffusion on a Ni(100) surface, the hydrogen adsorption and diffusion energetics and its dependence on the local coverage is described via a neural-network potential, where the training data are computed via periodic density functional theory (DFT) and include all relevant optimized diffusion and desorption paths, sampled by nudged elastic band optimizations and molecular dynamics simulations. Nuclear quantum effects, being crucial for processes involving hydrogen at low temperatures, are treated by RPMD. The diffusion rate constants are calculated with a combination of umbrella samplings employed to map the free energy profile and separate samplings of recrossing trajectories to obtain the transmission coefficient. The calculated diffusion rates for different temperatures and local environments are then combined and fitted into a kMC model allowing access to larger time and length scales. Our results demonstrate an outstanding performance for the trained neural network potential in reproducing reference DFT energies and forces. We report the effective diffusion rates for different temperatures and hydrogen surface coverages obtained via this recipe in good agreement with the experimental results. The method combination proposed in this study can be instrumental for a wide range of applications in materials science. [ABSTRACT FROM AUTHOR]

The anisotropic potential energy surface of the (H2)2 dimer represents a challenging problem for many-body methods. Here, we determine the potential energy curves of five different dimer configurations (T, Z, X, H, and L) using the lattice regularized diffusion Monte Carlo method and a number of approximate functionals within density functional theory (DFT), including advanced orbital-dependent functionals based on the random phase approximation (RPA). We assess their performance in describing the potential wells, bond distances, and relative energies. The repulsive potential wall is studied by looking at the relative stability of the different dimer configurations as a function of an applied force acting along the intermolecular axis. It is shown that most functionals within DFT break down at finite compression, even those that give an accurate description around the potential well minima. Only by including exchange within RPA, a qualitatively correct description along the entire potential energy curve is obtained. Finally, we discuss these results in the context of solid molecular hydrogen at finite pressures. [ABSTRACT FROM AUTHOR]

The bandgap is an inherent property of semiconductors and insulators, significantly influencing their electrical and optical characteristics. However, theoretical calculations using the density functional theory (DFT) are time-consuming and underestimate bandgaps. Machine learning offers a promising approach for predicting bandgaps with high precision and high throughput, but its models face the difficulty of being hard to interpret. Hence, an application of explainable artificial intelligence techniques to the bandgap prediction models is necessary to enhance the model's explainability. In our study, we analyzed the support vector regression, gradient boosting regression, and random forest regression models for reproducing the experimental and DFT bandgaps using the permutation feature importance (PFI), the partial dependence plot (PDP), the individual conditional expectation plot, and the accumulated local effects plot. Through PFI, we identified that the average number of electrons forming covalent bonds and the average mass density of the elements within compounds are particularly important features for bandgap prediction models. Furthermore, PDP visualized the dependency relationship between the characteristics of the constituent elements of compounds and the bandgap. Particularly, we revealed that there is a dependency where the bandgap decreases as the average mass density of the elements of compounds increases. This result was then theoretically interpreted based on the atomic structure. These findings provide crucial guidance for selecting promising descriptors in developing high-precision and explainable bandgap prediction models. Furthermore, this research demonstrates the utility of explainable artificial intelligence methods in the efficient exploration of potential inorganic semiconductor materials. [ABSTRACT FROM AUTHOR]

The electrocatalytic performance of VSe2 doped with nonmetals (NMs) was studied using density functional theory, in which NM atoms (C, N, O, P, S, F, Cl, Br, and I) replaced Se or V (denoted as NM@Se or NM@V). Notably, P@V and Br@V monolayers exhibit high catalytic hydrogen evolution reaction activity with the lowest ΔGH* = 0.08 eV and −0.03 eV, respectively, surpassing Pt (ΔGH* = −0.1 eV). By applying the scaling relationship of ΔGH* of H*, which is an intermediate for each volcano, the exchange current density diagrams are established. Based on thermodynamic analysis, P@V and Br@V monolayers produce exchange currents of about −1.42 and −0.70i0/(A cm−2), respectively. The oxygen evolution reaction activity of the I@Se monolayer (ηOER = 0.95 V) is the best among all the monolayers. Among the oxygen reduction reaction catalysts, the O@Se monolayer displays high activity with a low ηORR (0.82 V), which is even better than that of binary Pt and Pd alloys (0.9–0.87 V). [ABSTRACT FROM AUTHOR]

The large coercive electric field is the main obstacle for the application of wurtzite ferroelectrics in the memory devices. To explore the effective methods to reduce the coercive electric field of wurtzite ferroelectrics, in this work, after screening a series of possible candidates, (MC)1/(AlN)n (M = Ti, Zr) superlattices and anion P-doping are both chosen to regulate the switching energy barrier of wurtzite AlN based on the density functional theory calculations. It is found that the polarization switching energy barriers of wurtzite AlN gradually decrease by increasing the ratio of the MC (M = Ti, Zr) layer. When the ratio of the MC layer is up to 33%, the polarization switching energy barriers of (TiC)1/(AlN)n and (ZrC)1/(AlN)n are decreased by 68% and 55%, respectively, compared with that of pure wurtzite AlN. The anionic P-doping in AlN results in a 48% lower energy barrier. Also, the ferroelectric polarization in the designed superlattices and P-doped AlN is well maintained compared to pure AlN. Thus, the results show that the (MC)1/(AlN)n (M = Ti, Zr) superlattices and anion P-doping approaches are effective in improving the polarization switching properties of wurtzite AlN. [ABSTRACT FROM AUTHOR]

We developed a computational framework to extract the Raman spectra of nitrogen reduction and ammonia oxidation intermediates on high-entropy alloy (HEA) surfaces, integrating density functional theory with microstructural representations to account for the inherent lattice randomness in these materials. As a case study, we computed the Raman activities of intermediates (N2*, NNH*, N*, NH*, and NH3*) and H* adsorption on CoCuFeMoNi HEA surfaces. A comprehensive map of Raman peaks was generated and assigned to specific vibrational modes. The method highlighted the effects of lattice randomness on the Raman spectra compared to those of adsorbates on single-element catalysts. For instance, our results showed that the adsorbed N2 exhibits Raman modes that are dependent on whether the adsorption is vertical or horizontal. These peak differences could serve as unique fingerprints to identify nitrogen reduction reaction pathways. Moreover, it is also possible to detect surface poisoning by hydrogen, a common issue in reductive environments, due to the high-frequency peaks of H* compared to the typical N-metal stretching and bending frequencies. These results provide valuable references for identifying intermediates in nitrogen reduction and ammonia oxidation reactions, offering insights into reaction mechanisms and potential surface poisoning. This approach is generalizable to other reactions and surfaces in catalysis, provided that the relevant intermediates can be identified. [ABSTRACT FROM AUTHOR]

Methane (CH4) oxidation is an important reaction to reduce the greenhouse effect caused by incomplete combustion of CH4. Here, we explored the mechanism of CH4 oxidation catalyzed by CeO2 and Ni-doped CeO2, focusing on the redox properties of these catalyst surfaces, using density functional theory (DFT). We found that the barriers for CH4* activation and H2O* formation are correlated with the surface redox capacity, which is enhanced by Ni doping. Furthermore, the complete reaction mechanism is explored by DFT calculations and microkinetic simulations on bare and Ni-doped CeO2 surfaces. Our calculations suggest that the doping of Ni leads to a much higher overall reactivity, due to a balance between the CH4* activation and H2O* formation steps. These results provide insights into the CH4 oxidation mechanism and the intrinsic relationship between redox properties and the activity of CeO2 surfaces. [ABSTRACT FROM AUTHOR]

The CO reduction reaction (CORR) for the production of high-value-added multi-carbon (C2+) products is currently being actively investigated, where searching for high-efficiency catalysts with moderate CO intermediate binding strength and low kinetic barrier for C–C coupling poses a significant challenge. In this study, we employed density functional theory computations to design four synergistic coupling dual sites catalysts for CORR to C2 products, namely, TM-P@melon, by co-doping transition metals (TM = Mn, Fe, Co, and Ni) and phosphorus (P) into the polymeric carbon nitride (i.e., melon-CN). Mn–P@melon and Ni–P@melon exhibit higher selectivity toward C2H5OH and C2H6, respectively, with limiting potentials (C–C coupling kinetic energy barriers) of −0.43 V (0.52 eV) and −0.17 V (0.26 eV), respectively. The introduction of TM and P atoms not only narrows the band gap of melon-CN but also favors the coupling of CO and *CHO, providing an active site for C–C coupling, thus facilitating the catalytic reaction. Our work provides rational insights for the design of stable, low-cost, and efficient CORR dual sites catalysts that facilitate the sustainable production of high-value C2 chemicals and fuels. [ABSTRACT FROM AUTHOR]

Deep neural network-based deep potentials (DP), developed by Tuo et al., have been used to compute the thermodynamic properties of free aluminum clusters with accuracy close to that of density functional theory. Although Jarrold and collaborators have reported extensive experimental measurements on the melting temperatures and heat capacities of free aluminum clusters, no reports exist for finite-temperature ab initio simulations on larger clusters (N > 55 atoms). We report the heat capacities and melting temperatures for 32 clusters in the size range of 48–342 atoms, computed using the multiple histogram technique. Extensive molecular dynamics (MD) simulations at twenty four temperatures have been performed for all the clusters. Our results are in very good agreement with the experimental melting temperatures for 19 clusters. Except for a few sizes, the interesting features in the heat capacities have been reproduced. To gain insight into the striking features reported in the experiments, we used structural and dynamical descriptors such as temperature-dependent mean squared displacements and the Lindemann index. Bimodal features observed in Al116 and the weak shoulder seen in Al52 are attributed to solid–solid structural transitions. In confirmation of the earlier reports, we observe that the behavior of the heat capacities is significantly influenced by the nature of the ground state geometries. Our findings show that the sharp drop in the melting temperature of the 56-atom cluster is a consequence of the change in the geometry of Al55. Mulliken population analysis of Al55 reveals that the charge-induced local electric field is responsible for the strong bonding between core and surface atoms, leading to the higher melting temperature. Our calculations do not support the lower melting temperature observed in experimental studies of Al69. Our results indicate that Al48 is in a liquid state above 600 K and does not support the high melting temperature reported in the experiment. It turns out that the accuracy of the DP model by Tuo et al. is not reliable for MD simulations beyond 750 K. We also report low-lying equilibrium geometries and thermodynamics of 11 larger clusters (N = 147–342) that have not been previously reported, and the melting temperatures of these clusters are in good agreement with the experimental ones. [ABSTRACT FROM AUTHOR]

The increasing availability of graphics processing units (GPUs) for scientific computing has prompted interest in accelerating quantum chemical calculations through their use. However, the complexity of integral kernels for high angular momentum basis functions often limits the utility of GPU implementations with large basis sets or for metal containing systems. In this work, we report the implementation of f function support in the GPU-accelerated TeraChem software package through the development of efficient kernels for the evaluation of Hamiltonian integrals. The high efficiency of the resulting code is demonstrated through density functional theory (DFT) calculations on increasingly large organic molecules and transition metal complexes, as well as coupled cluster singles and doubles calculations on water clusters. Preliminary investigations into Ni(I) catalysis with DFT and the photochemistry of MnH(CH3) with complete active space self-consistent field are also carried out. Overall, our GPU-accelerated software appears to be well-suited for fast simulation of large transition metal containing systems, as well as organic molecules. [ABSTRACT FROM AUTHOR]

The technologically important Cu-chalcogenides, such as Cu2Se and CuInSe2, usually have relatively small band gaps. Achieving a reliable yet efficient description of the electronic properties has proven to be quite challenging for the popular exchange-correlation functionals of density functional theory, primarily due to the involvement of full-yet-shallow Cu-3d orbitals. In this study, we evaluate the applicability of several meta-generalized gradient approximation (GGA) functionals that have been recently developed. We find that the r2SCAN (regularized-restored strongly constrained and appropriately normed) functional significantly improves upon conventional local density approximation and GGA in terms of geometry and electronic band structure; however, there is still a notable discrepancy with experimental results due to the remaining delocalization error. This error is mitigated by combining r2SCAN with a Hubbard-like U correction applied to the Cu-3d orbitals. For predicting band gaps, both the TASK functional and the mBJ potential, when combined with the U correction, demonstrate similar accuracies with a mean absolute error of 0.17–0.19 eV. This accuracy is lower than that achieved with the many-body Hedin's GW approximation method but more accurate than that of hybrid functionals. Moreover, the r2SCAN+U approach well reproduces the phonon dispersion in CuInSe2, revealing a neglected computational problem in previous reports. We conclude that the meta-GGA+U approach represents a significant advancement by striking a balance between reliability and computational effort, and further efforts are still required to describe the Cu-3d orbitals more accurately. [ABSTRACT FROM AUTHOR]

Machine learning interatomic potentials (MLIPs) provide an optimal balance between accuracy and computational efficiency and allow studying problems that are hardly solvable by traditional methods. For metallic alloys, MLIPs are typically developed based on density functional theory with generalized gradient approximation (GGA) for the exchange–correlation functional. However, recent studies have shown that this standard protocol can be inaccurate for calculating the transport properties or phase diagrams of some metallic alloys. Thus, optimization of the choice of exchange–correlation functional and specific calculation parameters is needed. In this study, we address this issue for Al–Cu alloys, in which standard Perdew–Burke–Ernzerhof (PBE)-based MLIPs cannot accurately calculate the viscosity and melting temperatures at Cu-rich compositions. We have built MLIPs based on different exchange–correlation functionals, including meta-GGA, using a transfer learning strategy, which allows us to reduce the amount of training data by an order of magnitude compared to a standard approach. We show that r2SCAN- and PBEsol-based MLIPs provide much better accuracy in describing thermodynamic and transport properties of Al–Cu alloys. In particular, r2SCAN-based deep machine learning potential allows us to quantitatively reproduce the concentration dependence of dynamic viscosity. Our findings contribute to the development of MLIPs that provide quantum chemical accuracy, which is one of the most challenging problems in modern computational materials science. [ABSTRACT FROM AUTHOR]

Perovskite structures have attracted extensive attention in microelectromechanical systems and nanoelectromechanical systems devices due to the high piezoelectric response and the low dielectric constant. The piezoelectric and dielectric properties of the tetragonal BiFeO3/BaTiO3 superlattices grown along the c-axis direction are investigated using density functional theory (DFT) and density functional perturbation theory. The calculated results demonstrate that the (BiFeO3)n/(BaTiO3)n (n = 1–5) superlattices exhibit a profoundly increased piezoelectric response compared to their bulk structures. The (BiFeO3)2/(BaTiO3)2 possesses the highest piezoelectric d33 of 697 pC/N among known lead-free perovskite systems. Furthermore, the (BiFeO3)2/(BaTiO3)2 superlattice possesses a low dielectric ɛ33, and its d33 at 2% tensile strain is 16 times larger than that of an unstrained equilibrium structure. This demonstrates that biaxial tensile strain significantly enhances the piezoelectric response. Combining the special quasirandom structure method with DFT, the structures of a 0.5BiFeO3–0.5BaTiO3 solid solution are predicted, and its calculated d33 is 58 pC/N, which is much smaller than that of a (BiFeO3)2/(BaTiO3)2 superlattice. The results suggest that the (BiFeO3)2/(BaTiO3)2 superlattice might be a potential candidate for nonvolatile random access memory, transducers and actuators, and nanoscale electronic devices. [ABSTRACT FROM AUTHOR]

Accurate quantum mechanics based predictions of property trends are so important for material design and discovery that even inexpensive approximate methods are valuable. We use the alchemical integral transform to study multi-electron atoms and to gain a better understanding of the approximately quadratic behavior of energy differences between iso-electronic atoms in their nuclear charges. Based on this, we arrive at the following simple analytical estimate of energy differences between any two iso-electronic atoms, Δ E ≈ − (1 + 2 γ N e − 1 ) Δ Z Z ̄ . Here, γ ≈ 0.3766 ± 0.0020 Ha corresponds to an empirical constant, and Ne, ΔZ, and Z ̄ , respectively, to electron number, nuclear charge difference, and average. We compare the formula's predictive accuracy using experimental numbers and non-relativistic, numerical results obtained via density functional theory (pbe0) for the entire periodic table up to Radon. A detailed discussion of the atomic helium-series is included. [ABSTRACT FROM AUTHOR]

Entropy stabilized oxide of MgNiCoCuZnO5, also known as J14, is a material of active research interest due to a high degree of lattice distortion and tunability. Lattice distortion in J14 plays a crucial role in understanding the elastic constants and lattice thermal conductivity within the single-phase crystal. In this work, a neuroevolution machine learning potential (NEP) is developed for J14, and its accuracy has been compared to density functional theory calculations. The training errors for energy, force, and virial are 5.60 meV/atom, 97.90 meV/Å, and 45.67 meV/atom, respectively. Employing NEP potential, lattice distortion, and elastic constants is studied up to 900 K. In agreement with experimental findings, this study shows that the average lattice distortion of oxygen atoms is relatively higher than that of all transition metals in entropy-stabilized oxide. The observed distortion saturation in the J14 arises from the competing effects of minimum site distortion, which increases with increasing temperature due to enhanced thermal vibrations, and maximum site distortion, which decreases with increasing temperature. Furthermore, a series of molecular dynamics simulations up to 900 K are performed to study the stress–strain behavior. The elastic constants, bulk modulus, and ultimate tensile strength obtained from these simulations indicate a linear decrease in these properties with temperature, as J14 becomes softer owing to thermal effects. Finally, to gain some insight into thermal transport in these materials, with the so-developed NEP potential, and using non-equilibrium molecular dynamics simulations, we study the lattice thermal conductivity (κ) of the ternary compound MgNiO2 as a function of temperature. It is found that κ decreases from 4.25 W m − 1 K − 1 at room temperature to 3.5 W m − 1 K − 1 at 900 K. This suppression is attributed to the stronger scattering of low-frequency modes at higher temperatures. [ABSTRACT FROM AUTHOR]

The type-II Dirac fermions are characterized by a tilted Dirac cone and sustain exotic quantum transport phenomena. Here, we show the emergence of a type-II Dirac nodal loop in the SrVO 3 / SrTiO 3 quantum well structure by density functional theory calculations and symmetry arguments. When the spin–orbit coupling (SOC) is neglected, the unavoidable crossing between two t 2 g bands gives rise to a nodal loop in the Brillouin zone, which is protected by the mirror symmetry. Such a nodal loop is fully gapped by including SOC, which results in a large spin Hall effect due to large spin Berry curvatures. Our findings add the unexplored functionality to oxide quantum wells and offer a practical platform to explore the interplay between topological fermions and spin transport phenomena. [ABSTRACT FROM AUTHOR]

ZnO/g-C3N4 heterojunction modified with Ag nanoparticles (ZnO/CN/Ag) was synthesized by depositing ZnO nanorods/Ag nanoparticles onto g-C3N4 nanosheets. Under xenon lamp irradiation, 99% of Rhodamine B (RhB) was degraded by ZnO/CN/Ag-5% composite within 30 min, which was much higher than the degradation efficiency of ZnO and ZnO/CN. The synergistic effect of g-C3N4 and ZnO, along with the localized surface plasmon resonance effect of Ag NPs, contributes to the improvement of photocatalytic performance. Ag nanoparticle provides another charge transfer path from g-C3N4 to ZnO, which speeds up the separation of electron–hole pairs. Meanwhile, the catalyst had good stability and recyclability. Finite-difference time-domain method and the density functional theory were used to obtain the charge transfer process. The photodegradation process has been studied in depth. [ABSTRACT FROM AUTHOR]

Understanding the structures of phosphate glasses is important to many of their technological applications. Molecular dynamics simulations are commonly used to generate structure models of sodium phosphate glasses, and those with partial charge pairwise potentials have been successfully applied for modeling other network glasses, such as silicate and aluminosilicate glasses. In this work, we show that the addition of a three-body term is essential in regulating the intertetrahedral bond angles, as well as Qn speciation in comparison to experiments. Simulation results with and without three-body terms were compared and validated with experimental results, including neutron structure factors. Further comparison with glass structures fully relaxed with first-principles density functional theory was performed to evaluate the simulation results. The results show that the addition of three-body terms is vital for the modeling of phosphate glasses, and it can significantly improve the description of short- and medium-range structures and properties. [ABSTRACT FROM AUTHOR]

We present an ab initio method for computing vibro-polariton and phonon-polariton spectra of molecules and solids coupled to the photon modes of optical cavities. We demonstrate that if interactions of cavity photon modes with both nuclear and electronic degrees of freedom are treated on the level of the cavity Born–Oppenheimer approximation, spectra can be expressed in terms of the matter response to electric fields and nuclear displacements, which are readily available in standard density functional perturbation theory implementations. In this framework, results over a range of cavity parameters can be obtained without the need for additional electronic structure calculations, enabling efficient calculations on a wide range of parameters. Furthermore, this approach enables results to be more readily interpreted in terms of the more familiar cavity-independent molecular electric field response properties, such as polarizability and Born effective charges, which enter into the vibro-polariton calculation. Using corresponding electric field response properties of bulk insulating systems, we are also able to obtain the Γ point phonon-polariton spectra of two dimensional (2D) insulators. Results for a selection of cavity-coupled molecular and 2D crystal systems are presented to demonstrate the method. [ABSTRACT FROM AUTHOR]

Entropy stabilized oxide of MgNiCoCuZnO5, also known as J14, is a material of active research interest due to a high degree of lattice distortion and tunability. Lattice distortion in J14 plays a crucial role in understanding the elastic constants and lattice thermal conductivity within the single-phase crystal. In this work, a neuroevolution machine learning potential (NEP) is developed for J14, and its accuracy has been compared to density functional theory calculations. The training errors for energy, force, and virial are 5.60 meV/atom, 97.90 meV/Å, and 45.67 meV/atom, respectively. Employing NEP potential, lattice distortion, and elastic constants is studied up to 900 K. In agreement with experimental findings, this study shows that the average lattice distortion of oxygen atoms is relatively higher than that of all transition metals in entropy-stabilized oxide. The observed distortion saturation in the J14 arises from the competing effects of minimum site distortion, which increases with increasing temperature due to enhanced thermal vibrations, and maximum site distortion, which decreases with increasing temperature. Furthermore, a series of molecular dynamics simulations up to 900 K are performed to study the stress–strain behavior. The elastic constants, bulk modulus, and ultimate tensile strength obtained from these simulations indicate a linear decrease in these properties with temperature, as J14 becomes softer owing to thermal effects. Finally, to gain some insight into thermal transport in these materials, with the so-developed NEP potential, and using non-equilibrium molecular dynamics simulations, we study the lattice thermal conductivity (κ) of the ternary compound MgNiO2 as a function of temperature. It is found that κ decreases from 4.25 W m − 1 K − 1 at room temperature to 3.5 W m − 1 K − 1 at 900 K. This suppression is attributed to the stronger scattering of low-frequency modes at higher temperatures. [ABSTRACT FROM AUTHOR]

Based on two-component density functional theory integrated with the projector augmented-wave basis and incorporating both calculated and experimental data from Positron Annihilation Spectroscopy (PAS), this study introduces a novel method for identifying and analyzing specific types of vacancies when multiple types of vacancies are coexisting. This method was then tested on 4H-SiC irradiated by 300 keV C4+ ion beams. By calculating charge density to analyze positron annihilation lifetime spectroscopy and calculating wave functions to analyze slow positron-beam Doppler broadening spectroscopy, for the first time, silicon monovacancies (VSi) and carbon monovacancies (VC) in irradiated 4H-SiC were quantitatively detected separately, allowing them to be distinguished with high accuracy. In addition, a decreasing trend in the relative percentage of VC with increasing irradiation dose, consistent with that expected when irradiating with carbon ions, was also observed, illustrating both the effectiveness and potential of this method for broader applications in material defect analysis. This study not only addresses the challenges of identifying multiple coexisting vacancy types using PAS but also extends the applicability and depth of PAS in fields such as nuclear energy, aerospace, and semiconductors. [ABSTRACT FROM AUTHOR]

Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO2 are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in the down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO2 interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data are generated by density functional theory calculations. The obtained structures are in line with ab initio simulations and with experimental observations. Compared to a classical force field, the most recent reactive force field, the resulting configurations are vastly improved. Our potential is publicly available in an open-access repository. [ABSTRACT FROM AUTHOR]

The Hugoniot is the equation of state of a shock-compressed material and is a key part of high-pressure physics. One way of calculating it is via the Hugoniostat that has significant computational advantages over direct calculation via non-equilibrium molecular dynamics. We introduce a number of improvements to the Hugoniostat, which significantly reduce the run time and the number of atoms required for converged results. Consequently, ab initio Hugoniot calculations are tractable. We illustrate the benefits through simple model potentials and with density functional theory calculations of argon. [ABSTRACT FROM AUTHOR]

Most chemical processes happen at a local scale where only a subset of molecular orbitals is directly involved and only a subset of covalent bonds may be rearranged. To model such reactions, Density Functional Theory (DFT) is often inadequate, and the use of computationally more expensive correlated wavefunction (WF) methods is required for accurate results. Mixed-resolution approaches backed by quantum embedding theory have been used extensively to approach this imbalance. Based on the frozen density embedding freeze-and-thaw algorithm, we describe an approach to embed complete active space self-consistent field simulations run in the OpenMolcas code in a DFT environment calculated in CP2K without requiring any external tools. This makes it possible to study a local, active part of a chemical system in a larger and relatively static environment with a computational cost balanced between the accuracy of a WF method and the efficiency of DFT, which we test on environment–subsystem pairs. Finally, we apply the implementation to an oxygen molecule leaving an aluminum (111) surface and a ruthenium(IV) oxide (110) surface. [ABSTRACT FROM AUTHOR]

Point defects are thermodynamically stabilized in all crystalline materials, with increased densities negatively impacting the properties and performance of transition metal dichalcogenides (TMDs). While recent point defect reduction methods have led to considerable improvements in the optoelectronic properties of TMDs, there is a clear need for theoretical work to establish the lower limit of defect densities, as represented by thermal equilibrium. To that end, an ab initio and thermodynamic analysis of the equilibrium densities of intrinsic point defects in MoSe2 and WSe2 is presented. The intrinsic defect formation energies at the limits of the selenium and metal-rich regimes are determined by density functional theory (DFT) and then augmented with elemental chemical potential functions to determine temperature- and pressure-dependent formation energies. Equilibrium defect densities are determined for MSe, SeM, vM, and vSe, where M and v, respectively, represent the metal and the vacancy, as a function of synthesis temperature and pressure. The effects of vibrational free energy contributions and treatment of the DFT exchange–correlation potential are found to be non-negligible. Calculated equilibrium densities are several orders of magnitude below reported defect densities in TMDs made by chemical vapor deposition, chemical vapor transport, and flux methods, thereby establishing that current synthesis methods are either kinetically limited or impurity dominated. [ABSTRACT FROM AUTHOR]

A new population analysis, ADCHα-I, based on the interpolation between the Hirshfeld (H) and the iterative Hirshfeld (H-I) methods through a parameter α and on the atomic dipole moment corrected Hirshfeld (ADCH) methodology is proposed, in combination with the constrained dipole moment density functional theory (CD-DFT) previously developed, to determine the charge distributions of force fields. Following this approach, the electronic density of the isolated molecule is determined for the value of the dipole moment that reproduces the experimental dielectric constant, in order to incorporate through this property the effects of the surrounding molecules in the liquid, and to carry on this information to the molecular simulation, the new population analysis is built to obtain the set of charges that reproduces this dipole moment. By selecting α = 1/2, one is led to charges that are larger than the ones obtained through H and ADCH and smaller than those of H-I and that incorporate, at the local level, information about the response of isolated atoms to donate or to accept charge, which is not considered in ADCH. The results obtained for several liquid properties indicate that the combination of CD-DFT with this population analysis leads to a good description of the charge distributions in force fields used in molecular simulations. [ABSTRACT FROM AUTHOR]

Chemical reactions and vapor–liquid equilibria for molten lithium hydroxide (LiOH) were studied using molecular dynamics simulations and a deep potential (DP) model. The neural network for the model was trained on quantum density functional theory data for a range of conditions. The DP model allows simulations over timescales of hundreds of ns, which provide equilibrium compositions for the systems of interest. Single-phase NPT simulations of the liquid show the decomposition of LiOH into lithium oxide (Li2O) and dissolved water (H2O). These DP results were validated by direct ab initio molecular dynamics simulations that confirmed the accuracy of the model with respect to reaction kinetics and equilibrium properties of the melt. The reactive vapor–liquid behavior of this system was subsequently studied using direct coexistence interfacial DP simulations. Partial pressures of H2O in the vapor are found to be in close agreement with available experimental measurements. By fitting temperature-dependent expressions for the reaction equilibrium and Henry's law constants, the equilibrium composition for any given initial composition and temperature can be quantitatively modeled. For high initial concentrations of Li2O or H2O, mixtures of LiOH + Li2O/H2O are found to undergo phase separation. The present study illustrates how DP-based molecular dynamics simulations can be used for quantitative modeling of multiphase reactive behavior with the accuracy of the underlying ab initio quantum chemical methods. [ABSTRACT FROM AUTHOR]

The first-principles quantum chemical computations often scale as Nk (N = basis sets; k = 1–4 for linear scaling, Hartree–Fock or density functional theory methods), which makes the development of accurate pseudopotentials and efficient basis sets necessary ingredients in modeling of heavy elements such as lanthanides and actinides. Recently, we have developed 4f-in-core norm-conserving pseudopotentials and associated basis sets for the trivalent lanthanides [Lu et al., J. Chem. Theory Comput. 19, 82–96 (2023)]. In the present paper, we present a unified approach to optimize high-quality Gaussian basis sets for modeling and simulations of condensed-phase systems. The newly generated basis sets not only capture the low total energy and fairly reasonable condition number of overlap matrix of lanthanide-containing systems, but also exhibit good transferability and reproducibility. These advantages ensure the accuracy of the basis sets while avoiding linear dependency concern of atom-centered basis sets. The performance of the basis sets is further illustrated in lanthanide molecular and condensed-phase systems by using Gaussian-plane wave density functional approach of CP2K. These new basis sets can be of particular interest to model structurally complicated lanthanide molecules, clusters, solutions, and solid systems. [ABSTRACT FROM AUTHOR]

We investigate the density isobar of water and the melting temperature of ice using six different density functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base functionals. Notably, even the choice of damping can result in substantial differences. Overall, the outcomes obtained through density functional theory are not entirely satisfactory across most utilized functionals. All functionals exhibit significant deviations either in the melting temperature or equilibrium volume, with most of them even predicting an incorrect volume difference between ice and water. Our heuristic analysis indicates that a hybrid functional with 25% exact exchange and van der Waals damping averaged between zero and Becke–Johnson dampings yields the closest agreement with experimental data. This study underscores the necessity for further enhancements in the treatment of van der Waals interactions and, more broadly, density functional theory to enable accurate quantitative predictions for molecular liquids. [ABSTRACT FROM AUTHOR]

This study addresses the kinetic process of field evaporation of MgO assisted by ultrafast laser pulses combining density functional theory and molecular dynamics. A quantitative model is presented to describe the competitive evaporation of Mg and O ions under various conditions by comparing the activation barriers. The coordination number has a significant impact on the evaporation kinetics. The evaporation ratio of Mg to O rises with increasing DC field strength and laser intensity. Moreover, the energetics of evaporation is in correlation with photo-induced field ionization, revealing distinct mechanisms of evaporation for Mg and O. While Mg undergoes further ionization and field evaporation simultaneously, the evaporation of O is coupled with the relaxation of excited carriers. The final charge state of evaporated O is determined by the DC field strength rather than the laser intensity. Our findings provide insights into laser–matter interactions in ionic compounds and contribute to the development of atom probe techniques. [ABSTRACT FROM AUTHOR]

In the search for materials alternate to bulk HgCdTe for high performance infrared imaging applications, colloidal quantum dots (CQDs), particularly HgTe CQDs, have gained traction owing to acceptable detector performance with easy preparation and low cost. In this article, we evaluate α -Sn CQDs, an environmentally less reactive and less toxic alternative to HgTe, for infrared sensing applications. Ab initio density functional theory calculations are used to study the shape-dependent stability, electronic bandgap, and absorption coefficient of α -Sn CQD nanoparticles (NPs). We consider three possible CQD shape constructions—Wulff, shell-by-shell, and spherical. The CQD of Wulff construction is predicted to be the most stable. However, we find that the size, not the shape, of the NP has a strong effect on the bandgap and absorption coefficient. Consequently, a sharp absorption edge is expected even in an ensemble of CQDs with different shapes. Importantly, the shape determines the position of the band edges with respect to vacuum, and thus offers a possibility of choosing the shape to improve alignment with the energy levels of ligands to enable efficient drift transport, instead of a slower and less efficient hopping transport. [ABSTRACT FROM AUTHOR]

Organic electronics (OE) such as organic light-emitting diodes or organic solar cells represent an important and innovative research area to achieve global goals like environmentally friendly energy production. To accelerate OE material discovery, various computational methods are employed. For the initial generation of structures, a molecular cluster approach is employed. Here, we present a semi-automated workflow for the generation of monolayers and aggregates using the GFNn-xTB methods and composite density functional theory (DFT-3c). Furthermore, we present the novel D11A8MERO dye interaction energy benchmark with high-level coupled cluster reference interaction energies for the assessment of efficient quantum chemical and force-field methods. GFN2-xTB performs similar to low-cost DFT, reaching DFT/mGGA accuracy at two orders of magnitude lower computational cost. As an example application, we investigate the influence of the dye aggregate size on the optical and electrical properties and show that at least four molecules in a cluster model are needed for a qualitatively reasonable description. [ABSTRACT FROM AUTHOR]

We investigate the phase ordering (pattern formation) of systems of two-dimensional core–shell particles using Monte Carlo (MC) computer simulations and classical density functional theory (DFT). The particles interact via a pair potential having a hard core and a repulsive square shoulder. Our simulations show that on cooling, the liquid state structure becomes increasingly characterized by long wavelength density modulations and on further cooling forms a variety of other phases, including clustered, striped, and other patterned phases. In DFT, the hard core part of the potential is treated using either fundamental measure theory or a simple local density approximation, whereas the soft shoulder is treated using the random phase approximation. The different DFTs are benchmarked using large-scale grand-canonical-MC and Gibbs-ensemble-MC simulations, demonstrating their predictive capabilities and shortcomings. We find that having the liquid state static structure factor S(k) for wavenumber k is sufficient to identify the Fourier modes governing both the liquid and solid phases. This allows us to identify from easier-to-obtain liquid state data the wavenumbers relevant to the periodic phases and to predict roughly where in the phase diagram these patterned phases arise. [ABSTRACT FROM AUTHOR]

Using size-selected anion photoelectron spectroscopy and density functional theory, we investigated the structures and properties of fluorinated bimetallic clusters CoAuF1-2− and CuAuF1-2− and their neutrals. Both experimental and theoretical results show that in these cluster anions, Au behaves like a halogen atom. For example, the measured vertical detachment energies (VDEs) of CoAuF− (2.00 ± 0.08 eV) and CuAuF− (3.8 ± 0.1 eV) are close to those of CoF2− (2.12 ± 0.08 eV) and CuF2− (3.58 ± 0.08 eV), respectively. The theoretical results show that the geometries and electronic structures of CoAuF− and CuAuF− are similar to those of CoF2− and CuF2−. The natural population analysis and natural electron configuration analyses further confirm that the electronic properties of Au in MAuF− (M = Co, Cu) mimic those of MF2−. In addition, the electron localization function analyses show that the M-Au chemical bonds are similar to the corresponding M-F chemical bonds, providing evidence for the ionic nature of the interactions. When a second F atom is attached to the CoAuF− and CuAuF− clusters, the VDEs of the resulting CoAuF2− and CuAuF2− are 4.38 ± 0.08 eV and 3.71 ± 0.08 eV, respectively, indicating their superhalogen character as these values are higher than those of halogen anions. The results may be useful for understanding the properties of gold at the nanoscale that play an important role in catalysis and nanotechnology. [ABSTRACT FROM AUTHOR]

We present a detailed spectroscopic analysis of the first absorption band of the six possible conformers of 1,6-diphenyl-1,3,5-hexatriene, obtained by changing the configuration of trans double bonds to cis. To this end, we computed the absorption spectra using FCclasses 3.0 code. First, we assessed the performance of PBE0 and CAM-B3LYP density functional theory functionals with different basis sets to reproduce the experimental spectra. Additionally, we considered different models to compute the spectra. PBE0/def2tZVP with an adiabatic hessian model with internal coordinates yields results in very good agreement with experimental data. Subsequently, we analyzed the different contributions of vibronic transition to the spectral structure, correlating ground state conformation with spectral shape, and studied the effect of temperature on the absorption first band. [ABSTRACT FROM AUTHOR]

In this work, we systematically explore the effect of four-phonon (4ph) scattering on the lattice thermal conductivity (κ l) of γ -graphyne based on the atomic cluster expansion potential for carbon (C-ACE) combined with a phonon Boltzmann transport equation. The reliability of C-ACE in assessing the thermal transport properties of γ -graphyne is confirmed through comparing the results of phonon dispersion relation and κ 3 p h (only considering 3ph scattering) derived from C-ACE and density functional theory calculations. Regular residual analysis indicates that there might exist a strong 4ph interaction in γ -graphyne, and calculations further demonstrate κ 3 p h + 4 p h (considering 3ph scattering in an iterative solution and 4ph scattering in relaxation time approximation) is indeed reduced by 69.8% relative to κ 3 p h . From the analysis of scattering rates in γ -graphyne, one can intuitively observed that the 4ph scattering occupies a highly significant position in total phonon scattering, which greatly suppresses the κ l. The strong 4ph scattering in γ -graphyne is primarily due to the reflection symmetry selection rule less restricts 4ph scattering process for an out-of-plane flexural acoustic mode. The findings presented in this work demonstrate the reliability of C-ACE based accelerated calculations on the κ l of γ -graphyne, as well as reveal that the strong 4ph scattering in γ -graphyne significantly reduces its κ l , which will greatly promote the application of γ -graphyne and graphyne family in the field of thermoelectricity. [ABSTRACT FROM AUTHOR]

This study delves into the interplay of symmetry and structure in 2D systems to identify monolayers hosting valley physics together with the Rashba effect. Through high-throughput density functional theory calculations, 57 monolayers are identified exhibiting the Rashba effect, with the Rashba parameter α R spanning from 0 < α R < 2.0. The robustness of the Rashba parameters (α R) in these monolayers is primarily influenced by the physical parameters, highlighting the anti-crossing of Rashba-split bands and the Born effective charge (Z*). Among the 57 monolayers exhibiting the Rashba effect, the study identifies a subset of 23 monolayers presenting valley physics, demonstrating both in-plane and out-of-plane spin polarizations. The pronounced coupling of the valley and Rashba spin splitting is influenced by the in-plane and out-of-plane orbital contributions at the relevant K-points in the band spectra. In particular, the AB-type buckled structures feature these dual properties due to the presence of the broken inversion and mirror symmetries in them. Overall, the study eases the identification of monolayers with significant spin splitting and spin polarization, aiding in the design of high-performance 2D materials. [ABSTRACT FROM AUTHOR]

Designing coatings and films that can protect surfaces is important in a wide variety of applications from corrosion prevention to anti-fouling. These systems are challenging from a modeling perspective because they are invariably multicomponent, which quickly leads to an expansive design space. At a minimum, the system has a substrate, a film (often composed of a polymeric material), a ubiquitous carrier solvent, which may be either a vapor or liquid phase, and one or more contaminants. Each component has an impact on the effectiveness of coating. This paper focuses on films that are used as a barrier to surface contamination, but the results also extend to surface coatings that are designed to extract a low density species from the fluid phase as in liquid chromatography. A coarse-grained model is developed using Yukawa potentials that encompasses both repulsive and attractive interactions among the species. Classical density functional theory calculations are presented to show how contaminant adsorption is controlled by the molecular forces in the system. Two specific vectors through the parameter space are considered to address likely experimental manipulations that change either the solvent or the polymer in a system. We find that all the adsorption results can be unified by considering an appropriate combination of molecular parameters. As a result, these calculations provide a link between molecular interactions and film performance and may serve to guide the rational design of films. [ABSTRACT FROM AUTHOR]

Artificial photosynthesis holds immense promise for sustainable clean energy harvesting, with recent strides in material engineering with the earth abundant elements enabling efficient utilization of the visible solar spectrum for photoelectrochemical catalytic water splitting. Here, we have investigated the impact of substitutional Cu doping at all three cation sites in Ba2YNbO6 (BYN) using density functional theory calculations at the Heyd-Scuseria-Ernzerhof-06 level. One of the key findings is that the defect formation energy follows the hierarchy Nb > Ba > Y. The presence of an oxygen vacancy (OV) enhances the co-solubility of Cu substitution of Nb, particularly when placed outside the CuO6 unit, while it has a contrary effect for Y substitution. Cu replacement reduces the bandgap as Nb > Y ≫ Ba vis-à-vis pure BYN, while extending it into the visible part of the solar spectrum for Nb and Y replacement cases, albeit with OV causing a slight blue shift to them, without reducing the oxidation state of Cu due to strong charge-delocalization. Cu doping at Y and Nb sites retains the direct band transition character of BYN, a feature removed by OV. While all the bare Cu doped systems exhibit formation of a weak electron polaron, the placement of OV tends to annihilate this except for the system comprising first nearest neighbor placement of an OV relative to the Cu substitution at an Nb site. Notably, Cu doping at the Nb site significantly enhances optical activity, particularly ∼2.0–2.5 eV, resulting in promising candidates for photoelectrochemical catalysts. [ABSTRACT FROM AUTHOR]

We have implemented a cluster-continuum method using density functional theory to model water clusters and various charged species derived from water. The two aims of this study are to determine the minimal basis required for proper modeling of water autoionization and to determine the minimum number of explicit water molecules required to properly model the energetics of solvation. The thermodynamics of water autoionization converge following a modified power law to deliver chemically accurate values of the Gibbs energy change for autoionization with tractably small clusters. Convergence is slower and not exponential as assumed in previous work. We identify the n = 21 set of clusters as the first effectively bulk water like clusters that can capture the energetic influence of both the first and second solvation shells. In this cluster, a water molecule is encapsulated in the center of a closed shell of other water molecules that hydrogen bond to form five-membered rings. The total energy change for clusters with n ≥ 21 calculated using both the RPBE-D3 and ωB97X-D exchange–correlation functionals with the 6-311+G** basis set is shown to deliver good approximations to the free energy change at 298 K. This is true even though neither functional models the individual enthalpy or entropy contributions particularly well. [ABSTRACT FROM AUTHOR]

A newly parameterized combined density functional theory and multi-reference configuration interaction (DFT/MRCI) Hamiltonian, termed core-valence separation (CVS)-QE12, is defined for the computation of K-shell core-excitation and core-ionization energies. This CVS counterpart to the recently reported QE8 Hamiltonian [Costain et al., J. Chem. Phys, 160, 224106 (2024)] is parameterized by fitting to benchmark quality ab initio data. The definition of the CVS-QE12 and QE8 Hamiltonians differ from previous CVS-DFT/MRCI parameterizations in three primary ways: (i) the replacement of the BHLYP exchange–correlation functional with QTP17 to yield a balanced description of both core and valence excitation energies, (ii) the adoption of a new, three-parameter damping function, and (iii) the introduction of separate scaling of the core-valence and valence-valence Coulombic interactions. Crucially, the parameters of the CVS-QE12 Hamiltonian are obtained via fitting exclusively to highly accurate ab initio vertical core-excitation and ionization energies computed at the CVS-EOM-CCSDT level of theory. The CVS-QE12 Hamiltonian is validated against further benchmark computations and is found to furnish K-edge core vertical excitation and ionization energies exhibiting absolute errors ≤0.5 eV at low computational cost. [ABSTRACT FROM AUTHOR]

A low-cost approach for stochastically sampling static exchange during time-dependent Hartree–Fock-type propagation is presented. This enables the use of an excellent hybrid density functional theory (DFT) starting point for stochastic GW quasiparticle energy calculations. Generalized Kohn–Sham molecular orbitals and energies, rather than those of a local-DFT calculation, are used for building the Green function and effective Coulomb interaction. The use of an optimally tuned hybrid diminishes the starting point dependency in one-shot stochastic GW, effectively avoiding the need for self-consistent GW iterations. [ABSTRACT FROM AUTHOR]

Symmetry-adapted perturbation theory (SAPT) directly computes intermolecular interaction energy in terms of electrostatics, exchange-repulsion, induction/polarization, and London dispersion components. In SAPT based on Hartree–Fock ("SAPT0") or based on density functional theory, the most time-consuming step is the computation of the dispersion terms. Previous work has explored the replacement of these expensive dispersion terms with simple damped asymptotic models. We recently examined [Schriber et al. J. Chem. Phys. 154, 234107 (2021)] the accuracy of SAPT0 when replacing its dispersion term with Grimme's popular -D3 correction, reducing the computational cost scaling from O ( N 5 ) to O ( N 3 ). That work optimized damping function parameters for SAPT0-D3/jun-cc-pVDZ using estimates of the coupled-cluster complete basis set limit [CCSD(T)/CBS] on a 8299 dimer dataset. Here, we explore the accuracy of SAPT0-D3 with additional basis sets, along with an analogous model using -D4. Damping parameters are rather insensitive to basis sets, and the resulting SAPT0-D models are more accurate on average for total interaction energies than SAPT0. Our results are surprising in several respects: (1) improvement of -D4 over -D3 is negligible for these systems, even charged systems where -D4 should, in principle, be more accurate; (2) addition of Axilrod–Teller–Muto terms for three-body dispersion does not improve error statistics for this test set; and (3) SAPT0-D is even more accurate on average for total interaction energies than the much more computationally costly density functional theory based SAPT [SAPT(DFT)] in an aug-cc-pVDZ basis. However, SAPT0 and SAPT0-D3/D4 interaction energies benefit from significant error cancellation between exchange and dispersion terms. [ABSTRACT FROM AUTHOR]