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At least 811 records · Page 45
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Composition-property correlations in NiTi-based shape memory alloys from the first principles

Development of shape memory alloys (SMA) has been traditionally accomplished by means of extensive empirical efforts, with a very limited support from phenomenological models and related tools. We present a theoretical framework based on the Hamiltonian formalism, which connects important thermodynamic properties directly to the relative energies (the eigenvalues of the Hamiltonian), computed from the first principles using density functional theory. The estimates based on this formalism allow to establish correlations between composition and physical properties, such as relative energies, phase transition temperatures, hysteresis, thermal expansion, and other thermodynamic information. Importantly, estimates and correlations not only provide a fast answer, but also elucidate underpinning of thermodynamic features in terms of the electronic structure. Using this framework, we consider phase transformations and correlations between the selected properties and composition in the NiTi-based ternary and multicomponent shape memory alloys. Our theoretical guidance facilitates design and development of future alloys.

shape memory alloys↗
DOE OSTI2020

Exploring Ca–Ce–M–O (M = 3d Transition Metal) Oxide Perovskites for Solar Thermochemical Applications

Solar thermochemical (STC) processes hold promise as efficient ways to generate renewable fuels, fuel precursors, or chemical feedstocks using concentrated sunlight. Specifically, one actively researched approach is the two-step STC cycle, which uses a redox-active, off-stoichiometric, transition-metal oxide material to split water and/or CO 2 , generating H 2 and/or CO, respectively, or syngas (a combination of H 2 and CO). Identifying novel metal oxides that yield larger reduction extents (practically achievable off-stoichiometries) than the state-of-the-art CeO 2 is critical. Here, we explore the chemical space of Ca–Ce–M–O (M = 3d transition metal, except Cu and Zn) metal oxide perovskites, with Ca and/or Ce occupying the A site and M occupying the B site within an ABO 3 framework, as potential STC candidates. We use density functional theory (DFT)-based calculations and systematically evaluate the oxygen vacancy (VaO) formation energy (≈ enthalpy of reduction in an STC cycle), electronic properties, thermodynamic stability of CaMO 3 , CeMO 3 , and Ca 0.5 Ce 0.5 MO 3 perovskites, and the VaO formation energy within Ca 0.5 Ce 0.5 Ti 0.5 Mg 0.5 O 3 perovskite. We consider only Ca and/or Ce on the A site because of their similar size and the potential redox activity of Ce 4+ . If both Ce and M exhibit simultaneous reduction with Va O formation, the resulting perovskite could exhibit a larger entropy of reduction than a single cation reduction. The increased entropy produces increased reduction for fixed temperature, partial pressure of oxygen, and reduction enthalpy, and therefore increased STC efficiency. Importantly, we identify Ca 0.5 Ce 0.5 MnO 3 , Ca 0.5 Ce 0.5 FeO 3 , and Ca 0.5 Ce 0.5 VO 3 to be promising candidates based on their Va O formation energy and thermodynamic (meta)stability. Moreover, based on our calculated on-site magnetic moments, electron density of states, and electron density differences between pristine and defective structures, we find Ca 0.5 Ce 0.5 MnO 3 to exhibit simultaneous reduction of both Ce 4+ (A-site) and Mn 3+ (B-site), highlighting a particularly promising candidate for STC applications with a predicted higher entropy of reduction than CeO 2 . Lastly, we extract metrics that govern the trends in Va O formation energies, such as standard reduction potentials, and provide pointers for further experimental and theoretical studies, which will enable the design of improved materials for the STC cycle.

14 SOLAR ENERGY↗
DOE OSTI2024

Zentropy theory for accurate prediction of free energy, volume, and thermal expansion without fitting parameters

Based on statistical mechanics, a macroscopically homogeneous system, i.e., a single phase in the present context, is composed of many independent configurations that the system embraces. The macroscopical properties of the system are determined by the properties and statistical probabilities of those configurations with respect to external conditions. The volume of a single phase is thus the weighted sum of the volumes of all configurations. Consequently, the derivative of the volume to temperature of a single phase depends on both the derivatives of the volumes of every configuration to temperature and the derivatives of their statistical probabilities to temperature, with the latter introducing nonlinear emergent behaviors. It is shown that the derivative of the volume to the temperature of the single phase can be negative, i.e., negative thermal expansion, due to the symmetry-breaking non-ground-state configurations with smaller volumes than that of the ground-state configuration and the rapid increase of the statistical probabilities of the former, and negative thermal expansion can be predicted without fitting parameters from the zentropy theory that combines quantum mechanics and statistical mechanics with the free energy of each configuration predicted from quantum mechanics and the partition function of each configuration calculated from its free energy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ethane Dehydrogenation on Single and Dual Centers of Ga-modified γ-Al 2 O 3

Density-functional theory calculations and microkinetic analysis are used to investigate the efficacy of Ga-modified γ-Al 2 O 3 (110) surfaces for the catalytic dehydrogenation of ethane and elucidate the synergy between Ga and Al sites. The model surfaces are modified by either Ga grafting or doping. We consider and analyze numerous active sites and rank them using microkinetic analysis. The kinetic parameters obtained from microkinetic modelling are compared with experimental values for ethane dehydrogenation over Ga 2 O 3 -Al 2 O 3 mixed oxides prepared by co-precipitation. The dominant reaction pathway proceeds via heterolytic C-H bond dissociation to a surface proton and a metal-carbanion intermediate that undergoes β-hydride elimination. We find that grafted Ga sites are catalytically inactive. In contrast, Ga-doped sites exhibit fivefold enhancement in catalytic activity when compared to the sites on pristine Al 2 O 3 , owed to the synergy between neighboring AlIII and GaIV sites. Furthermore, we model and investigate the effect of surface hydroxylation, demonstrate how surface water interferes with the aforementioned synergy between AlIII and GaIV sites and discuss the implications for the catalytic activity of the modified surfaces. Here, an increase in the partial pressure of H 2 O significantly increases the apparent activation energies of dehydrogenation and interestingly changes the most active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology

Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol–disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol–disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. Furthermore, as an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol–disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Homogeneous quantum electrodynamic turbulence

The electromagnetic field equations and Dirac equations for oppositely charged wave functions are numerically time-integrated using a spatial Fourier method. The numerical approach used, a spectral transform technique, is based on a continuum representation of physical space. The coupled classical field equations contain a dimensionless parameter which sets the strength of the nonlinear interaction (as the parameter increases, interaction volume decreases). For a parameter value of unity, highly nonlinear behavior in the time-evolution of an individual wave function, analogous to ideal fluid turbulence, is observed. In the truncated Fourier representation which is numerically implemented here, the quantum turbulence is homogeneous but anisotropic and manifests itself in the nonlinear evolution of equilibrium modal spatial spectra for the probability density of each particle and also for the electromagnetic energy density. The results show that nonlinearly interacting fermionic wave functions quickly approach a multi-mode, dynamic equilibrium state, and that this state can be determined by numerical means.

Shebalin, John V.↗
DOE OSTI2023

Extreme sensitivity of higher-order interatomic force constants and thermal conductivity to the energy surface roughness of exchange-correlation functionals

In this Letter, we report that the fourth-order interatomic force constants (4th-IFCs) are significantly sensitive to the energy surface roughness of exchange-correlation (XC) functionals in density functional theory calculations. This sensitivity, which is insignificant for the second- (2nd-) and third-order (3rd-) IFCs, varies for different functionals in different materials and can cause misprediction of thermal conductivity by several times of magnitude. As a result, when calculating the 4th-IFCs using the finite difference method, the atomic displacement needs to be taken large enough to overcome the energy surface roughness, in order to accurately predict phonon lifetime and thermal conductivity. We demonstrate this phenomenon on a benchmark material (Si), a high-thermal conductivity material (BAs), and a low thermal conductivity material (NaCl). For Si, we find that the LDA, PBE, and PBEsol XC functionals are all smooth to the 2nd- and 3rd-IFCs but all rough to the 4th-IFCs. This roughness can lead to a prediction of nearly one order of magnitude lower thermal conductivity. For BAs, all three functionals are smooth to the 2nd- and 3rd-IFCs, and only the PBEsol XC functional is rough for the 4th-IFCs, which leads to a 40% underestimation of thermal conductivity. For NaCl, all functionals are smooth to the 2nd- and 3rd-IFCs but rough to the 4th-IFCs, leading to a 70% underprediction of thermal conductivity at room temperature. In conclusion, with these observations, we provide general guidance on the calculation of 4th-IFCs for an accurate thermal conductivity prediction.

42 ENGINEERING↗
NASA NTRS1971

Characterization of impulse noise and analysis of its effect upon correlation receivers

A noise model is formulated to describe the impulse noise in many digital systems. A simplified model, which assumes that each noise burst contains a randomly weighted version of the same basic waveform, is used to derive the performance equations for a correlation receiver. The expected number of bit errors per noise burst is expressed as a function of the average signal energy, signal-set correlation coefficient, bit time, noise-weighting-factor variance and probability density function, and a time range function which depends on the crosscorrelation of the signal-set basis functions and the noise waveform. A procedure is established for extending the results for the simplified noise model to the general model. Unlike the performance results for Gaussian noise, it is shown that for impulse noise the error performance is affected by the choice of signal-set basis functions and that Orthogonal signaling is not equivalent to On-Off signaling with the same average energy.

Houts, R. C.↗
DOE OSTI2024

Influence of excess silicon on polytype selection during metal-mediated epitaxy of GaN nanowires

For this work, we have examined the origins of polytype selection during metal-mediated molecular-beam epitaxy of GaN nanowires (NWs). High-angle annular dark-field scanning transmission electron microscopy reveals [111]-oriented zinc blende (ZB) NWs and [0001]-oriented wurtzite (WZ) NWs, with SixNy at the interface between individual NWs and the Si (001) substrate. Quantitative energy dispersive x-ray spectroscopy reveals a notably higher Si concentration of 7.0% ± 2.3% in zinc blende (ZB) NWs than 2.3% ± 1.2% in wurtzite (WZ) NWs. Meanwhile, density functional theory calculations show that incorporation of 8 at. % Si on the Ga sublattice inverts the difference in formation energies between WZ and ZB GaN, such that the ZB polytype of GaN is stabilized. This identification of Si and other ZB polytype stabilizers will enable the development of polytype heterostructures in a wide variety of WZ-preferring compounds.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Decreasing the Overpotential of Aprotic Li-CO 2 Batteries with the In-Plane Alloy Structure in Ultrathin 2D Ru-Based Nanosheets

We report the aprotic Li-CO 2 battery is emerging as a promising energy storage technology with the capability of CO 2 fixation and conversion. However, its practical applications are still impeded by the large overpotential. Herein, the general synthesis of a series of ultrathin 2D Ru-M (M = Co, Ni, and Cu) nanosheets by a facile one-pot solvothermal method is reported. As a proof-of-concept application, the representative RuCo nanosheets are used as the cathode catalysts for Li-CO 2 batteries, which demonstrate a low charge voltage of 3.74 V, a small overpotential of 0.94 V, and hence a high energy efficiency of 75%. Ex/in situ studies and density functional theory calculations reveal that the excellent catalytic performance of RuCo nanosheets originates from the enhanced adsorption toward Li and CO 2 during discharge as well as the elevated electron interaction with Li 2 CO 3 during charge by the in-plane RuCo alloy structure. This work indicates the feasibility of boosting the electrochemical performance of Li-CO 2 batteries by in-plane metal alloy sites of ultrathin 2D alloy nanomaterials.

25 ENERGY STORAGE↗
DOE OSTI2020

Comparison of GW band structure to semiempirical approach for an FeSe monolayer

We present the G 0 W 0 band structure, core levels, and deformation potential of monolayer FeSe in the paramagnetic phase based on a starting mean field of the Kohn-Sham density functional theory (DFT) with the Perdew, Burke, and Ernzerhof functional. We find the GW correction increases the bandwidth of the states forming the M pocket near the Fermi energy, while leaving the Γ pocket roughly unchanged. We then compare the G 0 W 0 quasiparticle band energies with the band structure from a simple empirical + A approach, which was recently proposed to capture the renormalization of the electron-phonon interaction going beyond DFT in FeSe, when used as a starting point in density functional perturbation theory. We show that this empirical correction succeeds in approximating the GW nonlocal and dynamical self-energy in monolayer FeSe and reproduces the GW band structure near the Fermi surface, the core energy levels, and the deformation potential (electron-phonon coupling).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Extraction of neutron-capture cross sections on 92 Zr using the charge-exchange Oslo method

The 93 Nb (𝑡, 3 He ) reaction at 115 MeV/nucleon was studied to demonstrate that nuclear level densities and 𝛾-ray strength functions can be extracted from charge-exchange reactions at intermediate energies using the Oslo technique. The matrix of excitation energy in 93 Zr, reconstructed from the (𝑡, 3 He ) reaction, versus the energy of 𝛾 rays emitted by the excited 93 Zr nuclei, was obtained in an experiment with the S800 Spectrograph operated in coincidence with the GRETINA 𝛾 -ray detector. The extracted level density and 𝛾 -ray strength function obtained by applying the Oslo method to this matrix were used to estimate the 92 Zr⁢(𝑛,𝛾) ⁢93 Zr cross section by combining the new results with other experimental data and theoretical calculations for 𝐸⁢1 and 𝑀⁢1 strength functions at higher energies. Good agreement with direct measurements of the 92 Zr⁢(𝑛,𝛾)⁢ 93 Zr cross section was found. The contribution from the upbend in the extracted 𝛾-ray strength function was important to achieve the consistency, as the neutron-capture cross section without this contribution is significantly below the direct measurements otherwise. Since charge-exchange reactions at intermediate energies have long been used for extracting Gamow-Teller strengths, the successful demonstration of the charge-exchange Oslo method enables experiments in which (𝑛,𝛾) cross sections and Gamow-Teller strengths can be measured simultaneously, which is of benefit for astrophysical studies.

90 ≤ A ≤ 149↗
DOE OSTI2025

Mineral dissolution by dimeric complexes

Mineral dissolution is typically thought to occur by the detachment of monomeric building blocks of the crystal structure, although direct evidence is rare. Using in situ high-speed atomic force microscopy to examine step-edge retreat dynamics at high resolution, we report that the dissolution of gibbsite in alkaline solutions occurs mainly by the release of aluminate dimers, which subsequently dissociate into the monomeric species that dominate the solution. Here, the observed dissolution anisotropy is readily explained by this mechanism, which was further supported by density functional tight-binding simulations of detachment activation energies. Recognition that such polynuclear dissolution mechanisms exist may enable an improved understanding of processes regulating mineral dissolution rates in nature and industry.

atomic force microscopy↗
DOE OSTI2024

Machine-Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

To cope with legacy greenhouse gas emissions and to achieve net-zero emissions by 2050, the U.S. Department of Energy (DOE) is funding efforts to develop direct air capture (DAC), a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been well studied as DAC sorbent materials due to their tunable structural and compositional properties. Thermodynamic simulations using force fields are often used to provide predictions of a material’s performance in many separations. However, these force fields often make assumptions about bonds and the physics of the adsorption process. A new class of force fields called machine-learned force fields (MLFFs) use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). In this work, models were developed to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using MLFFs. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption in several MOFs.

Findley, John↗
DOE OSTI2023

Ab Initio Molecular Dynamics Study of Pd Nucleation on a γ-Al 2 O 3 Surface

We perform ab initio molecular dynamics (AIMD) free energy simulations to investigate the stability and dynamics of dispersed Pd atoms and subnanometer clusters on γ-Al 2 O 3 . We consider nucleation for varying Pd loadings on dry γ-Al 2 O 3 (100), dry γ-Al 2 O 3 (110) and wet γ-Al 2 O 3 (110) and examine temperature, entropy and water coverage effects on the stability and dynamics of Pd clusters. At low Pd loading (1.07 Pd atoms/nm 2 on the (100) and 0.75 Pd/nm 2 on the (110)), association of the Pd atoms is thermodynamically unfavorable on the dry surfaces and almost thermoneutral on the wet (110) owing to water blocking of the AlIII defect sites. In agreement with past studies, the Pd atoms are quite mobile but the diffusion free energy barriers do not increase with the water coverage, as previously suggested, but rather remain similar in magnitude owing to translational entropy at finite temperatures. At these Pd loadings, oxidized Pd(II) atoms on the wet (110) surface are dispersed and immobile. At higher Pd loading (1.12 and 1.5 Pd atoms/nm 2 ) on the (110) surface, Pd 3 and Pd 4 clusters are thermodynamically very stable and form fast. At the 1.12 Pd atoms/nm 2 loading, the formation of the Pd 3 cluster is two orders of magnitude slower when the surface is hydroxylated as the system becomes somewhat trapped before the dinuclear cluster Pd 2 collides with a Pd atom.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Modeling Pb(II) Adsorption on Mineral Surfaces: Bridging Density Functional Theory and Experiment with Thermodynamic Insights

Despite decades of work on aqueous lead (Pb) adsorption on a-Fe2O3 (hematite) and a-Al2O3 (alumina), gaps between measurements and modeling obscure molecular-level understanding. Achieving well-matched geometries between theory and experimental for mineral-water interfaces is a hurdle, as surface functional group type and distribution must be accounted for in determining mechanisms. Additionally, computational methods that can describe the substrate are often not appropriate to capture aqueous effects. Progress requires focusing on well-studied and relevant systems, such as key facets (001),(012), and (110) of hematite and alumina, and ubiquitous contaminants such as aqueous Pb. In the past, bulk-parametrized bond-valence principles were used to rationalize Pb(II) adsorption trends. These approaches can break down at surfaces, where flexible bonding environments and adsorption-induced surface relaxations play a critical role. Here, we adapt and apply a density functional theory (DFT) and thermodynamics framework, integrating DFT-calculated energies with experimental data and electrochemical principles, to predict Pb(II) adsorption. Our model results capture trends across the full set of surfaces and predict that inner-sphere Pb(II) sorption on (001) alumina varies from unfavorable to weakly favorable across a range of pH conditions. This aligns with experiment insights that Pb(II) interacts at that surface through outer-sphere interactions. Extending to Fe(II) adsorption, we demonstrate a coverage-dependent site preference, potentially explaining disorder in overlayers grown by the oxidative adsorption of Fe(II) on hematite (001).

lead contamination↗