Finite Difference Interpolation for Reduction of Grid-Related Errors in Real-Space Pseudopotential Density Functional Theory
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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Bond Dissociation Energies in Heavy Element Chalcogen and Halogen Small Molecules
Thermodynamic properties including bond dissociation energies, heats of formation, and gas phase acidities for the hydrides and dimers of chalcogens and halogens: H 2 Y, HX, Y 2 and X 2 for Y = Se, Te, At and X = Br, I, and At have been predicted using the Feller-Peterson-Dixon composite correlated molecular orbital theory approach. A full 4-component CCSD(T) approach was used to calculate the spin-orbit effects on thermodynamic properties, except for Se 2 where the AoC-DHF value was used due to strong multireference effects in Se 2 for the SO calculations. The calculated results show that the At 2 bond dissociation energy is quite small, 19.5 kcal/mol, with much of the low bond energy due to spin orbit effects. H 2 Po is not predicted to be stable to dehydrogenation to Po + H 2 in terms of the free energy at 298 K. In the gas phase, HAt is predicted to be a stronger acid than H 2 SO 4 . Here, the current results provide insights into potential difficulties in the actual experimental observation of such species for heavy elements.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Correlation between Spin and Orbital Dynamics during Laser-Induced Femtosecond Demagnetization
Spin and orbital angular momenta are two intrinsic properties of an electron and are responsible for the physics of a solid. How the spin and orbital evolve with respect to each other on several hundred femtoseconds is largely unknown, but it is at the center of laser-induced ultrafast demagnetization. In this paper, we introduce a concept of the spin–orbital correlation diagram, where spin angular momentum is plotted against orbital angular momentum, much like the position-velocity phase diagram in classical mechanics. We use four sets of highly accurate time-resolved X-ray magnetic circular dichroism data to construct four correlation diagrams for iron and cobalt. To our surprise, a pattern emerges. The trace on the correlation diagram for iron is an arc, and at the end of demagnetization, it has a pronounced cusp. The correlation diagram for cobalt is different and appears more linear but with kinks. We carry out first-principles calculations with two different methods: time-dependent density functional theory (TDDFT) and time-dependent Liouville density functional theory. These two methods agree that the experimental findings for both Fe and Co are not due to experimental errors. It is the spin–orbit coupling that correlates the spin dynamics to the orbital dynamics. Microscopically, Fe and Co have different orbital occupations, which leads to distinctive correlation diagrams. We believe that this correlation diagram presents a useful tool to better understand spin and orbital dynamics on an ultrafast time scale. A brief discussion on the magnetic anisotropy energy is also provided.
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Organic Photovoltaic Efficiency Predictor: Data-Driven Models for Non-Fullerene Acceptor Organic Solar Cells
In the design of organic solar cells, there has been a need for materials with high power conversion efficiencies. Scharber’s model is commonly used to predict efficiency; however, it exhibits poor performance with new non-fullerene acceptor (NFA) devices, since it was designed for fullerene-based devices. In this work, an empirical model is proposed that can be a more accurate alternative for NFA organic solar cells. Additionally, many screening studies use computationally expensive methods. A model based on using semiempirical simplified time-dependent density functional theory (sTD-DFT) as an alternative method can accelerate the calculations and yield a similar accuracy. The models presented in this paper, termed organic photovoltaic efficiency predictor (OPEP) models, have shown significantly lower errors than previous models, with OPEP/B3LYP yielding errors of 1.53% and OPEP/sTD- DFT of 1.55%. As a result, the proposed computational models can be used for the fast and accurate screening of new high-efficiency NFAs/donor pairs.
14 SOLAR ENERGY↗
Strongly Correlated States of Transition Metal Spin Defects: The Case of an Iron Impurity in Aluminum Nitride
We investigate the electronic properties of an exemplar transition metal impurity in an insulator, with the goal of accurately describing strongly correlated defect states. Here, we consider iron in aluminum nitride, a material of interest for hybrid quantum technologies, and we carry out calculations with quantum embedding methods, density matrix embedding theory (DMET) and quantum defect embedding theory (QDET), and with spin-flip time-dependent density functional theory (TDDFT). We show that both DMET and QDET accurately describe the ground state and low-lying excited states of the defect and that TDDFT yields photoluminescence spectra in agreement with experiments. In addition, we provide a detailed discussion of the convergence of our results as a function of the active space used in the embedding methods, thus defining a protocol to obtain converged data directly comparable with experiments.
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Effects of the Grazing Incidence Geometry on X-ray Photon Correlation Spectroscopy Measurements
X-ray photon correlation spectroscopy (XPCS) is a versatile tool to measure dynamics on the nanometer to micrometer scale in bulk samples. XPCS has also been applied in grazing incidence (GI) geometry to examine the dynamics of surface layers. However, considering GI scattering experiments more universally, the GI geometry leads to a superposition of signals due to reflection and refraction effects, also known from the distorted-wave Born approximation (DWBA). In this paper, the impact of these reflection and refraction effects on the correlation analysis is determined experimentally by measuring grazing incidence transmission XPCS (GT-XPCS) and grazing incidence XPCS (GI-XPCS) simultaneously for a thin film sample, showing non-equilibrium dynamics. Here the results of the GI and GT geometry comparisons are combined within the framework of the standardly applied, simplified DWBA. These calculations allow identifying the main contributions of the detected signal from the leading scattering terms along the out-of-plane direction q z , which dominate the measured intensity pattern on the detector. In combination with the calculation of the non-linear effect of refraction in GTSAXS and GISAXS, it is possible to identify experimental conditions that can be chosen to run experiments and data analysis as close as possible to transmission XPCS and to explain which limitations for data interpretations are observed. Consequently, the beam exposure can be significantly reduced by using GI geometry only. Calculations of experimental settings prior to experiments are detailed to determine suitable q z regions for a variety of material systems measured in bulk-sensitive GI-XPCS experiments, allowing us to determine the scaling behavior of typical decay times as a function of q that is comparable to the scaling behavior obtained in distortion-free GT-XPCS or transmission XPCS experiments.
36 MATERIALS SCIENCE↗
Pseudocapacitive Titanium Oxynitride Nanowires for Ultrahigh Capacitance Supercapacitors
High-quality, multifunctional two-dimensional (2D) titanium oxynitide (TiNO) thin films and one-dimensional (1D) TiNO nanowires have been synthesized using a pulsed laser deposition, a simple, fast, and congruent evaporation method. First-principles calculations as a function of surface orientation and termination indicate that surface oxidation of TiNO nanowires can stabilize the (110) orientation observed experimentally. The specific capacitance value for the TiNO nanowire samples (2725 mF/cm 2 ) has been found to be nearly six times more than that of the TiNO thin film samples (400 mF/cm 2 ), which is attributed to the high packing density of TiNO nanowires over a given area. The nanowire samples have also been found to exhibit a significantly higher energy density (1.35 μWh/cm 2 ) than the TiNO thin-film samples (0.33 μWh/cm 2 ). Thus, the TiNO material system in thin-film and nanowire forms has been demonstrated to be a promising candidate for use as an electrode material in supercapacitors and other charge-storage applications.
capacitors↗
Machine learning guided discovery of ternary compounds involving La and immiscible Co and Pb elements
Abstract Ternary compounds with an immiscible pair of elements are relatively unexplored but promising for novel quantum materials discovery. Exploring what third element and its ratio that can be added to make stable ternary compounds out of an immiscible pair of elements remains a great challenge. In this work, we combine a machine learning (ML) method with ab initio calculations to efficiently search for the energetically favorable ternary La-Co-Pb compounds containing immiscible elements Co and Pb. Three previously reported structures are correctly captured by our approach. Moreover, we predict a ground state La 3 CoPb compound and 57 low-energy La-Co-Pb ternary compounds. Attempts to synthesize La 3 CoPb via multiple techniques produce mixed or multi-phases samples with, at best, ambiguous signals of the predicted lowest-energy La 3 CoPb and the second lowest-energy La 18 Co 28 Pb 3 phases. The calculated results of Gibbs free energy are consistent with experiments, and will provide very useful guidance for further experimental synthesis.
36 MATERIALS SCIENCE↗
CFD-based design optimization of ducted hydrokinetic turbines
Abstract Hydrokinetic turbines extract kinetic energy from moving water to generate renewable electricity, thus contributing to sustainable energy production and reducing reliance on fossil fuels. It has been hypothesized that a duct can accelerate and condition the fluid flow passing the turbine blades, improving the overall energy extraction efficiency. However, no substantial evidence has been provided so far for hydrokinetic turbines. To investigate this problem, we perform a CFD-based optimization study with a blade-resolved Reynolds-averaged Navier–Stokes (RANS) solver to explore the design of a ducted hydrokinetic turbine that maximizes the efficiency of energy extraction. A gradient-based optimization approach is utilized to effectively deal with the high-dimensional design space of the blade and duct geometry, with gradients being calculated through the adjoint method. The final design is re-evaluated through higher-fidelity unsteady RANS (URANS) simulations. Our optimized ducted turbine achieves an efficiency of about 54% over a range of operating conditions, higher than the typical 46% efficiency of unducted turbines.
13 HYDRO ENERGY↗
Reactivity descriptors in acid catalysis: acid strength, proton affinity and host–guest interactions
Brønsted acids mediate chemical transformations via proton transfer to bound species and interactions between the conjugate anion and bound cationic intermediates and transition states that are also stabilized by van der Waals forces within voids of molecular dimensions in inorganic hosts. This Feature Article describes the relevant descriptors of reactivity in terms of the properties of acids and molecules that determine their ability to donate and accept protons and to reorganize their respective charges to optimize their interactions at bound states. The deprotonation energy (DPE) of the acids and the protonation energy (Eprot) of the gaseous analogs of bound intermediates and transition states reflect their respective properties as species present at non-interacting distances. These properties accurately describe the reactivity of acids of a given type, such as polyoxometalates (POM) with a given type of addenda atom but different central atoms and heterosilicates, for different families of reactions. They do not fully capture, however, differences among acid types (e.g., Mo and W POM, heterosilicates, mineral acids) for diverse types of chemical transformations (e.g., elimination, isomerization, dimerization, condensation). The incompleteness of such descriptors reflects their inability to describe how protonated molecular species and conjugate anions restructure their respective charges when present as a binding pair at interacting distances. Such interaction energies represent electrostatic forces that depend on charge distributions in the cations and anions and the ability to reorganize the distributions to maximize the interactions. In the case of deprotonation, the electrostatic and charge reorganization components of DPE for various acids solely reflect the ability of the conjugate anion to accept and distribute the negative charge, a characteristic unique of each type of solid acid and specifically of the composition of its extended conjugate anion framework. The energy required to accept and rearrange the positive charge in bound intermediates and transition states reflects, in turn, their respective ability to recover the ionic and covalent components of DPE, the energy required to detach proton from conjugate anions. The DPE components and the recovery fractions together lead to a modified DPE, which captures only the part of DPE that remains unrecovered by the ion-pair interactions at bound intermediates and transition states, as the unifying descriptor for broad families of acids and reactions. The electrostatic and charge reorganization energies involved in these general descriptors are placed in historical context by assessing their connections to the heuristics of hard–soft acid–base displacements. Further development of these concepts requires benchmarking and extension of electrostatic and reorganization components of energies for a more diverse set of reaction types and acid families and advancement of methods for more efficient calculations of electrostatic interactions. Reactivity descriptors must also account for dispersive interactions between host cavities and guest molecules, requiring a framework analogous to the one described here for ion-pair interactions; these dispersive interactions depend on the fit between their shapes and sizes as well as their ‘‘structural stiffness’’ that determines the ability to modify the shapes of molecules and voids to minimize free energy. Entropy considerations and estimates of their dependence on properties of catalysts and molecules are also required for accurately determining Gibbs free energies that ultimately determine reaction rates.
Deshlahra, Prashant↗
Advancing energy storage through solubility prediction: leveraging the potential of deep learning
Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.
25 ENERGY STORAGE↗
Uncovering novel liquid organic hydrogen carriers: a systematic exploration of chemical compound space using cheminformatics and quantum chemical methods
We present a comprehensive, in silico-based discovery approach to identifying novel liquid organic hydrogen carrier (LOHC) candidates using cheminformatics methods and quantum chemical calculations. We screened over 160 billion molecules from ZINC15 and GDB-17 chemical databases for structural similarity to known LOHCs and employed a data-driven selection criterion connecting molecular features with dehydrogenation enthalpy. This scoring criterion effectively predicts dehydrogenation enthalpies from SMILES strings, streamlining the LOHC screening process. After rigorous screening and down-selection, we compiled a database of 3000 dehydrogenation reactions for the most promising LOHC candidates, setting the stage for future selection based on kinetics and catalysis. This work demonstrates the significant impact of integrating quantum chemistry and cheminformatics in materials discovery, accelerating the selection process while reducing experimental efforts and time. By proposing new molecules as prospective LOHC candidates, our study provides a valuable resource for researchers and engineers in the development of advanced LOHC systems and showcases a successful approach for high-throughput discovery, contributing to more efficient and sustainable energy storage solutions.
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Tricyclononenes and tricyclononadienes as efficient monomers for controlled ROMP: understanding structure–propagation rate relationships and enabling facile post-polymerization modification
Grubbs 3rd-generation (G3) pre-catalyst-initiated ring-opening metathesis polymerization (ROMP) remains an indispensable tool in the polymer chemist's toolbox. Tricyclononenes (TCN) and tricyclononadienes (TCND) represent under-explored classes of monomers for ROMP that have the potential to both advance fundamental knowledge (e.g., structure-polymerization kinetics relationships) and serve as practical tools for the polymer chemist (e.g., post-polymerization functionalization). In this work, a library of TCN and TCND imides, monoesters, and diesters, along with their exo-norbornene counterparts, were synthesized to compare their behaviors in G3-initiated ROMP. Real-time 1 H NMR was used to study their polymerization kinetics; propagation rates ( k p ) were extracted for each monomer. To understand the relationships between monomer structure and ROMP propagation rates, density functional theory methods were used to calculate a variety of electronic and steric parameters for each monomer. While electronic parameters (e.g., HOMO energy levels) correlated positively with the measured k p values, steric parameters generally gave improved correlations, which indicates that monomer size and shape are better predictors for k p than electronic parameters for this data set. Furthermore, the TCND diester—which contains an electron-deficient cyclobutene that is resistant to ROMP—and its polymer p(TCND) are shown to be highly reactive toward DBU-catalyzed conjugate addition reactions with thiols, providing a protecting- and activating-group free strategy for post-polymerization modification.
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Sensing depths in frequency domain thermoreflectance
In this work, a method is developed to calculate the length into a sample to which a Frequency Domain Thermoreflectance (FDTR) measurement is sensitive. Sensing depth and sensing radius are defined as limiting cases for the spherically spreading FDTR measurement. A finite element model for FDTR measurements is developed in COMSOL multiphysics and used to calculate sensing depth and sensing radius for silicon and silicon dioxide samples for a variety of frequencies and laser spot sizes. The model is compared to experimental FDTR measurements. Design recommendations for sample thickness are made for experiments where semi-infinite sample depth is desirable. For measurements using a metal transducer layer, the recommended sample thickness is three thermal penetration depths, as calculated from the lowest measurement frequency.
36 MATERIALS SCIENCE↗
Non-Richtmyer–Meshkov instability ejecta production based on shallow bubble collapse
The study of shock-driven ejecta production has focused on Richtmyer–Meshkov instability (RMI) growth from geometric features of the material surface. Extensive study of this mechanism under both single- and multiple-shock conditions has found that the ejected mass tends to be closely associated with the shocked surface phase, and its temperature is not dramatically greater than the hydrodynamic shock temperature of the bulk. In this work, we propose and demonstrate a new ejecta production mechanism that can occur under multiple-shock conditions based on the collapse of bubbles near the free surface of the material. This mechanism produces ejected mass that is much greater in quantity than observed in the RMI case. The particles are much hotter than predicted by the shock Hugoniot state, and the ejected mass does not appear to be strongly dependent upon initial surface finish. The ejecta source extends into the material with no clear remaining free surface. We name this mechanism Shallow Bubble Collapse (SBC) and discuss the conditions under which it activates. We demonstrate resolved modeling methods that enable the calculation, design, and study of SBC as a mechanism and perform a series of experiments to compare with the models. Under some multiple-shock conditions, SBC ejection produces ten times more ejected mass than RMI growth.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Diffractive optical elements for multiplexing structured laser beams
We consider methods for obtaining a multitude of structured laser beams (multiplexing) from an illuminating beam (both structured and unstructured) with the help of diffractive optical elements (DOEs). An approach of ‘intelligent multiplexing’ is proposed to describe and develop the methods. A DOE is calculated that forms a set of five diffraction orders located in a line. An example of focusing a set of doughnut-shaped azimuthally polarised laser beams using a diffraction beam splitter is presented. Efficient multiplexing of first-order vortex beams in a two-dimensional region is implemented by a two-dimensional diffraction grating. An approach is proposed and realised in which the transmission functions of a two-dimensional diffraction beam splitter and the elements forming structured laser beams with specified parameters are combined into the transmission function of one element. Such DOEs can be used in optical communication systems for encoding and decoding data. The possibility of using binary curved fork-shaped gratings for the formation of doughnut-shaped three-dimensional vortex beams, which are detected outside the focal plane, is demonstrated. This approach provides additional advantages for safe data transmission; it can be used in laser processing of materials and in laser manipulation applications. (paper)
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Molecular beam mass spectrometry measurements of vibrationally excited N 2 in the effluent of an atmospheric plasma jet: a comparison with a state-to-state kinetic model
Vibrationally excited N 2 molecules are suggested to be one of the possible key species responsible for the observed synergistic effects in plasma catalysis for NH 3 synthesis. To assess the impact of vibrationally excited species in plasma-catalysis, quantitative measurements near interfaces are required, which remains challenging. In this letter, we report spatially resolved measurements of vibrationally excited N 2 in the effluent of an atmospheric pressure plasma jet by molecular beam mass spectrometry (MBMS). The mass spectrometry signals as a function of electron energy of the ionizer were fitted with the effective electron-impact ionization cross section of N 2 (v) considering the vibrational distribution function as determined by a detailed vibrational level resolved plasma kinetic calculation. Here, the reported method presented in this letter shows the capability of MBMS to measure vibrationally excited species of N 2 near interfaces when the vibrational distribution function is known or assumed and shows excellent agreement with state-to-state kinetic models of N 2 (v).
42 ENGINEERING↗