Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Engineering Papers
Results for “energy density functional”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
In-situ structure and catalytic mechanism of NiFe and CoFe layered double hydroxides during oxygen evolution
NiFe and CoFe (MFe) layered double hydroxides (LDHs) are among the most active electrocatalysts for the alkaline oxygen evolution reaction (OER). Herein, we combine electrochemical measurements, operando X-ray scattering and absorption spectroscopy, and density functional theory (DFT) calculations to elucidate the catalytically active phase, reaction center and the OER mechanism. We provide the first direct atomic-scale evidence that, under applied anodic potentials, MFe LDHs oxidize from as-prepared α-phases to activated γ-phases. The OER-active γ-phases are characterized by about 8% contraction of the lattice spacing and switching of the intercalated ions. DFT calculations reveal that the OER proceeds via a Mars van Krevelen mechanism. The flexible electronic structure of the surface Fe sites, and their synergy with nearest-neighbor M sites through formation of O-bridged Fe-M reaction centers, stabilize OER intermediates that are unfavorable on pure M-M centers and single Fe sites, fundamentally accounting for the high catalytic activity of MFe LDHs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Highly reversible oxygen redox in layered compounds enabled by surface polyanions
Oxygen-anion redox in lithium-rich layered oxides can boost the capacity of lithium-ion battery cathodes. However, the over-oxidation of oxygen at highly charged states aggravates irreversible structure changes and deteriorates cycle performance. Here, we investigate the mechanism of surface degradation caused by oxygen oxidation and the kinetics of surface reconstruction. Considering Li 2 MnO 3 , we show through density functional theory calculations that a high energy orbital (lO 2p’ ) at under-coordinated surface oxygen prefers over-oxidation over bulk oxygen, and that surface oxygen release is then kinetically favored during charging. We use a simple strategy of turning under-coordinated surface oxygen into polyanionic (SO 4 ) 2- , and show that these groups stabilize the surface of Li 2 MnO 3 by depressing gas release and side reactions with the electrolyte. Experimental validation on Li 1.2 Ni 0.2 Mn 0.6 O 2 shows that sulfur deposition enhances stability of the cathode with 99.0% capacity remaining (194 mA h g -1 ) after 100 cycles at 1 C. Our work reveals a promising surface treatment to address the instability of highly charged layered cathode materials.
25 ENERGY STORAGE↗
A theoretical investigation of the hydrolysis of uranium hexafluoride: the initiation mechanism and vibrational spectroscopy
Depleted uranium hexafluoride (UF 6 ), a stockpiled byproduct of the nuclear fuel cycle, reacts readily with atmospheric humidity, but the mechanism is poorly understood. Here we compare several potential initiation steps at a consistent level of theory, generating underlying structures and vibrational modes using hybrid density functional theory (DFT) and computing relative energies of stationary points with double-hybrid (DH) DFT. A benchmark comparison is performed to assess the quality of DH-DFT data using reference energy differences obtained using a complete-basis-limit coupled-cluster (CC) composite method. The associated large-basis CC computations were enabled by a new general-purpose pseudopotential capability implemented as part of this work. Dispersion-corrected parameter-free DH-DFT methods, namely PBE0-DH-D3(BJ) and PBE-QIDH-D3(BJ), provided mean unsigned errors within chemical accuracy (1 kcal mol -1 ) for a set of barrier heights corresponding to the most energetically favorable initiation steps. The hydrolysis mechanism is found to proceed via intermolecular hydrogen transfer within van der Waals complexes involving UF 6 , UF 5 OH, and UOF 4 , in agreement with previous studies, followed by the formation of a previously unappreciated dihydroxide intermediate, UF 4 (OH) 2 . The dihydroxide is predicted to form under both kinetic and thermodynamic control, and, unlike the alternate pathway leading to the UO 2 F 2 monomer, its reaction energy is exothermic, in agreement with observation. Finally, harmonic and anharmonic vibrational simulations are performed to reinterpret literature infrared spectroscopy in light of this newly identified species.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The role of sub-surface hydrogen on CO 2 reduction and dynamics on Ni(110): An ab initio molecular dynamics study
The catalytic reduction of carbon dioxide is a crucial step in many chemical industrial reactions, such as methanol synthesis, the reverse water-gas shift reaction (rWGS) and formic acid synthesis. Here, we investigate the role of bulk hydrogen, where hydrogen atoms are found deep inside a metal surface as opposed to subsurface ones, upon CO 2 reduction over a Ni(110) surface using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. While it has previously been shown that subsurface hydrogen stabilizes CO 2 and can aid in overcoming reaction barriers, the role of bulk hydrogen is less studied and thus unknown with regard to CO 2 reduction. Furthermore, we find that the presence of bulk hydrogen can significantly alter the electronic structure of the Ni(110) surface, particularly the work function and d-band center, such that CO 2 adsorbs more strongly to the surface and is more easily reduced. Our results show an enhanced CO 2 dissociation in the presence of bulk hydrogen, shedding light on a hitherto underappreciated mechanistic pathway for CO 2 reduction on metal surfaces.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Representations and strategies for transferable machine learning improve model performance in chemical discovery
Strategies for machine-learning (ML)-accelerated discovery that are general across material composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets such as open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (~1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the Periodic Table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to the graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the group number alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data are limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the Periodic Table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the Periodic Table, a property we expect to be broadly useful for other material domains.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers
Neural network potentials (NNPs) can greatly accelerate atomistic simulations relative to ab initio methods, allowing one to sample a broader range of structural outcomes and transformation pathways. In this work, we demonstrate an active sampling algorithm that trains an NNP that is able to produce microstructural evolutions with accuracy comparable to those obtained by density functional theory, exemplified during structure optimizations for a model Cu–Ni multilayer system. We then use the NNP, in conjunction with a perturbation scheme, to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP. The code to implement our active learning strategy and NNP-driven stochastic shear simulations is openly available at https://github.com/pnnl/Active-Sampling-for-Atomistic-Potentials .
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Phase space distribution functions and energy distributions of dark matter particles in haloes
ABSTRACT For a spherical dark matter halo with isotropic velocity distribution, the phase space distribution function (DF), the energy distribution, and the density profile form a set of self-consistent description of its equilibrium state, and knowing one is sufficient to determine the other two. The Navarro–Frenk–White density profile (NFW profile) is known to be a good approximation to the spherically averaged density distribution in simulated haloes. The DARKexp energy distribution is also known to compare well with the simulated energy distribution. We present a quantitative assessment of the NFW and DARKexp fits to the simulated DF and energy distribution for a wide range of haloes in a dark-matter-only simulation from the IllustrisTNG Project. As expected, we find that the NFW fits work well except at low energy when the density at small radii deviates from the NFW profile. Further, the NFW and DARKexp fits have comparable accuracy in the region where both fit well, but the DARKexp fits are better at low energy because they require matching of the central gravitational potential. We also find an approximate relation between the energy scale parametrizing the DARKexp energy distribution and that defined by the characteristic density and radius of the NFW profile. This relation may be linked to the relaxation process during halo formation.
Astronomy & Astrophysics↗
Machine learning approach for vibronically renormalized electronic band structures
Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Quantifying Large Lattice Relaxations in Photovoltaic Devices
Temporal variations of Cu(In,Ga)Se 2 photovoltaic device properties during light exposure at various temperatures and voltage biases for times up to 100 h are analyzed using the kinetic theory of large lattice relaxations. Open-circuit voltage and p-type doping increased with charge injection and decreased with temperature at low injection conditions. Furthermore, lattice relaxation can account for both trends and activation energies extracted from the data are approximately 0.9 and 1.2 eV for devices with lower and higher sodium content, respectively. In these devices, increased sodium content resulted in higher initial p-type doping with greater stability. First-principles calculations providing revised activation energies for the (V Se -V Cu ) complex suggest that this defect does not account for the metastability observed here.
14 SOLAR ENERGY↗
First-principles theory of the Dirac semimetal Cd 3 As 2 under Zeeman magnetic field
Time-reversal broken Weyl semimetals have attracted much attention recently, but certain aspects of their behavior, including the evolution of their Fermi surface topology and anomalous Hall conductivity with Fermi-level position, have remained underexplored. A promising route to obtain such materials may be to start with a nonmagnetic Dirac semimetal and break time-reversal symmetry via magnetic doping or magnetic proximity. Here we explore this scenario in the case of the Dirac semimetal Cd 3 As 2 , based on first-principles density-functional calculations and subsequent low-energy modeling of Cd 3 As 2 in the presence of a Zeeman field applied along the symmetry axis. We clarify how each four-fold degenerate Dirac node splits into four Weyl nodes, two with chirality ±1 and two higher-order nodes with chirality ±2. Using a minimal k · p model Hamiltonian whose parameters are fit to the first-principles calculations, we detail the evolution of the Fermi surfaces and their Chern numbers as the Fermi energy is scanned across the region of the Weyl nodes at fixed Zeeman field. We also compute the intrinsic anomalous Hall conductivity as a function of Fermi-level position, finding a characteristic inverted-dome structure. Cd 3 As 2 is especially well suited to such a study because of its high mobility, but the qualitative behavior revealed here should be applicable to other Dirac semimetals as well.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate
We report a molecular dynamics study of ab initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium across the phase transition. We find that the emergence and disappearance of the macroscopic polarization is driven by dipolar switching at the nanometer scale. We also computed the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. Often, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction experiments. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation associated with thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Landscape of nuclear deformation softness with spherical quasiparticle random-phase approximation
We investigate the stability and softness of nuclei against quadrupole, octupole, and hexadecapole deformation. By applying the spherical Skyrme-force Hartree-Fock Bardeen-Cooper-Schrieffer quasiparticle random phase approximation, we diagnose ground-state deformation when imaginary solutions are obtained, i.e., the spherical ground state collapses. We also calculate the multipole polarizability in spherical nuclei with no collapse, as a measure of softness. This numerically light and theoretically sound method is found able to capture deformation patterns across the nuclide chart. As a result, the connection between the intrinsic shape of nuclei and the dynamics of their low-lying collective states is established and the role of shell structure is discussed.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Theoretical Correlation of Elemental Distribution of Nd and Pr in Ce-Fe-B Microstructure With Hard Magnetic Properties
Relatively resource-rich but property inferior-Ce-Fe-B magnet can be improved by partial replacement of Ce by Nd and/or Pr. In addition to the amount of Nd/Pr, their distribution profile in microstructure plays an important role. From our first principles density functional theory (DFT) calculation, the substitution energy of Ce by Pr/Nd is negative in Ce 2 Fe 14 B (2:14:1) while that for laves phase, CeFe 2 is positive, implying that Nd/Pr stabilize 2:14:1 and suppress the formation of the CeFe 2 phase. Further, micromagnetic simulation indicates that homogenized distribution of Nd/Pr improves squareness of demagnetization curve, while core (Ce-rich)-shell (Nd/Pr-rich) 2:14:1 grain structure enhances coercivity. Magnetic properties of Ce-Fe-B can be optimized by manipulating distribution profile of chemical element in microstructure based on their subtle difference in thermodynamic property, which is an effective pathway to design optimized chemical composition and processing route for high-performance magnet.
36 MATERIALS SCIENCE↗
Graph-theoretical KMC Framework for Calculating Effective Diffusivity in TPBAR Components: Effective Diffusivity of Tritium in α-Zr Grain Boundaries
We report the development of a computer simulation tool based on the Graph-theoretical kinetic Monte Carlo (GT-KMC) approach that can be used to simulate diffusion and derive effective diffusivity of species in complex structures of TPBAR components. The GT-KMC framework was implemented in AKSOME, an on-lattice self-learning kinetic Monte Carlo tool developed at PNNL and was previously used to study solute diffusion in metal alloys. This newly developed tool will be helpful in simulating atom diffusion and extracting their effective diffusivities in grain boundaries and interfaces in various TPBAR components. Once benchmarked, the tool will be used to simulate the diffusion of tritium (hydrogen) along grain boundaries in α-Zr using the activation energy barrier data previously obtained from Density Functional Theory calculations.
36 MATERIALS SCIENCE↗
Boron adatom adsorption on graphene: A case study in computational chemistry methods for surface interactions
Though weak surface interactions and adsorption can play an important role in plasma processing and materials science, they are not necessarily simple to model. A boron adatom adsorbed on a graphene sheet serves as a case study for how carefully one must select the correct technique from a toolbox of computational chemistry methods. Using a variety of molecular dynamics potentials and density functional theory functionals, we evaluate the adsorption energy, investigate barriers to adsorption and migration, calculate corresponding reaction rates, and show that a surprisingly high level of theory may be necessary to verify that the system is described correctly.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Likelihood-Based Particle Identification in the Short-Baseline Near Detector
Accurate particle identification is crucial in any high-energy physics experiment, allowing scientists to understand the unique interactions and mechanisms at play in a detector. In this project, I develop and study a new particle identification (PID) algorithm for the Short-Baseline Near Detector, a likelihood-based approach, different from out current $\chi^2$ method. A likelihood estimation offers a more physically motivated strategy for PID. The distribution random energy losses of charged particles traveling through a medium are described by the Vavilov probability density function. By using this model, we can account for random energy losses and construct likelihood functions specific to each particle type, potentially enabling a more accurate method for PID.
Vanderwaal, Sophia [U. Alabama, Huntsville] (ORCID↗
Understanding the Electronic Behavior of f-Element Intermetallics
Due to their partially filled f-orbitals, lanthanide and actinide intermetallic materials exhibit intriguing electronic, structural, and magnetic properties. These materials provide a unique platform to explore exotic magnetic and quantum phenomena that arise from their strongly correlated f-electrons, offering insights into f-electron behavior. Incorporating light elements such as boron and carbon can significantly impact the electronic, chemical, and physical properties of these materials, with varying influence between lanthanides and actinides due to the more localized f-electrons in lanthanides. In this work, lanthanide-based intermetallic single crystals containing light atoms were synthesized using the molten metal flux growth method. In this method, one or more low-melting metals such as aluminum, gallium, tin, and bismuth are used in excess as the reaction medium. This solution-state approach allows for lower reaction temperatures compared to solid-state reactions and enables the isolation of kinetic products rather than thermodynamically stable compounds. The synthesized crystals were analyzed using an extensive array of analytical and computational characterization techniques, including single crystal X-ray diffraction, scanning electron microscopy, energy-dispersive spectroscopy, magnetization measurements, and density functional theory calculations. These analyses provided detailed insights into the structural and magnetic properties of the synthesized materials.
37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗