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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Automation of Overset Structured Mesh Generation onBoundary Representation Geometries

A scheme is presented for the automatic generation of structured overset meshes ongeometries that are defined by Boundary Representation (BRep) solids. The surface meshsystem consists of face, edge and node meshes corresponding to the three respective basicBRep entity types. A cut-cell method is introduced to improve robustness of the on-geometrydetermination test for a face mesh grid point. A geometric component tagging scheme is utilizedto enhance local grid point distribution on a configuration with a large range of geometricscales. A cap grid topology is automatically utilized around the trailing edge of wing and tailtips to enhance mesh quality and to enable more effective surface coverage. Robustness of thehyperbolic surface marching method is improved by replacing the point projection scheme witha walking scheme. Relaxation of surface grid spacing at concave corners enables automatedhigh quality hyperbolic volume mesh creation. Domain connectivity is automatically performedon the surface mesh system. A variety of test cases are presented including a re-entry capsule,two models of the Juncture Flow Experiment wing-body, five rotorcraft concept vehicles, andvarious components of the High-Lift Common Research Model from the High-Lift PredictionWorkshop 4.

TTT↗
DOE OSTI2026

Size dependent lattice pseudosymmetry for frustrated decahedral nanoparticles

Geometric frustration—where geometry prevents simultaneous satisfaction of local interactions—generates pseudosymmetry and emergent behaviors across physical and biological systems. At the nanoscale, pseudosymmetric features in crystalline materials manifest as local strain and distortion, but how they depend on particle size and control structural stability remains unclear. Here, we report the first study of a size-dependent crossover in pseudosymmetry in multi-twinned gold nanoparticles (NPs), combining four-dimensional scanning transmission electron microscopy with nanoscale strain mapping grounded in continuum solid mechanics. Analysis of more than 20 decahedral NPs (20–55 nm) reveals pronounced heterogeneity in multiple modes of in-plane strain and displacement field in small NPs as five tetrahedral grains close the geometric gap, without extended defects. With increasing particle size, strain fields homogenize across grains and local phases shift from predominantly low-symmetry body-centered tetragonal motifs at small sizes to face-centered cubic character approaching the bulk limit. We identify a crossover particle size of ~35 nm, well below bulk, correlating with a transition from modified-Wulff shapes to pentagonal bipyramids, consistent with finite element predictions. This quantitative framework for mapping size-dependent strain and pseudosymmetry enables precise design and control of functional crystalline solids and phase transformation for catalysis, photonics, electronics, and energy storage.

Lin, Oliver [University of Illinois at Urbana-Cham↗
NASA NTRS2012

Finding Near-Earth Asteroid (NEA) Destinations for Human Exploration: Implications for Astrobiology

The current number of known potential NEA targets for HSF is limited to those objects whose orbital characteristics are similar to that of the Earth. This is due to the projected capabilities of the exploration systems currently under consideration and development at NASA. However, NEAs with such orbital characteristics often have viewing geometries that place them at low solar elongations and thus are difficult to detect from the vicinity of Earth. While ongoing ground-based surveys and data archives maintained by the NEO Program Observation Program Office and the Minor Planet Center (MPC) have provided a solid basis upon which to build, a more complete catalog of the NEO population is required to inform a robust and sustainable HSF exploration program. Since all the present NEO observing assets are currently confined to the vicinity of the Earth, additional effort must be made to provide capabilities for detection of additional HSF targets via assets beyond Earth orbit. A space-based NEO survey telescope located beyond the vicinity of the Earth, has considerable implications for planetary science and astrobiology. Such a telescope will provide foundational knowledge of our Solar System small body population and detect targets of interest for both the HSF and scientific communities. Data from this asset will yield basic characterization data on the NEOs observed (i.e., albedo, size determination, potential for volatiles and organics, etc.) and help down select targets for future HSF missions. Ideally, the most attractive targets from both HSF and astrobiology perspectives are those NEAs that may contain organic and volatile materials, and which could be effectively sampled at a variety of locations and depths. Presented here is an overview of four space-based survey concepts; any one of which after just a few years of operation will discover many highly accessible NEO targets suitable for robotic and human exploration. Such a space-based survey mission will reveal incredible returns for several disciplines including: exploration, in situ resource utilization, planetary defense, and science. Of particular, interest to the scientific

Landis, Rob↗
NASA NTRS2018

Conceptual Aerodynamic Design of a Tail-Cone Thruster System Under Axi-Symmetric Inlet Distortion

This paper presents a conceptual design of a tail-cone thruster system which is operating under an axisymmetric inlet distortion. An effort to realize the targeted fuel burn saving that was proposed in NASA's STARC_ABL aircraft design is made through a CFD (Computational Fluid Dynamics)-based design approach. This method employs three iterative steps to exploit the CFD tools until the design requirements are met: a quasi-2D through-flow model to design the fan/EGV (Exit Guide Vane), a 3-D RANS (Reynolds Averaging Navier-Stokes) simulation of the single blade row to account for the inlet/fan and the EGV/nozzle interaction, and a 3-D RANS simulation of the airframe with a propulsor installed - propulsion airframe integration (PAI). The design requirements which include the thrust, and shaft power of the propulsor are matched throughout the evaluations coming from two CFD domains, i.e., the turbo-machinery and the PAI. During the switch between these different computational domains, the inlet and exit profiles are matched via the correction factors of the body-force model. The present tail-cone thruster (TCT) aerodynamic design leverages a low-pressure ratio fan (FPR=1.2 to approximately 1.25) of which the camber-line angles are predicted by a quasi-2D through-flow model. The quasi-2D model is derived to analyze the radially distorted flow resulting from the ingested boundary layer at the inlet. It also estimates the appropriate velocity vectors of the metal angles of the fan and EGV which is subjected to different types of vortex at the fan exit. The baseline geometry is revisited and its internal flow-path and exhaust cone are redesigned to illustrate the strong correlation among the components of the propulsor in the PAI domain. The peak efficiency point of the fan/EGV with respect to the blade counts, also known as solidity, and rotational speed is chosen for the cruise condition via parametric studies. The corresponding performance maps are presented. The resulting performance metrics of the new conceptual design of the BLI (Boundary Layer Ingestion) propulsor are analyzed and compared with these of the baseline in the PAI aspect. Finally, ideas of the CFD based design of a BLI propulsor are discussed based on the observations drawn from the numerical results.

Inlet↗
DOE OSTI2023

Insights into Waterflooding in Hydrocarbon-Bearing Nanochannels of Varying Cross Sections from Mesoscopic Multiphase Flow Simulations

Waterflooding is one of the geotechnique used to recover fuel sources from nanoporous geological formations. The scientific understanding of the process that involves the multiphase flow of nanoconfined fluids, however, has lagged, mainly due to the complex nanopore geometries and chemical compositions. To enable benchmarked flow of nanoconfined fluids, the architected geomaterials, such as synthesized mesoporous silica with tunable pore shapes and surface chemical properties, are used for designing and conducting experiments and simulations. This work uses a modified many-body dissipative particle dynamics (mDPD) model with accurately calibrated parameters to perform parametric flow simulations for studying the influences of waterflooding driven power, pore shape, surface roughness, and surface wettability on the multiphase flow in heptane-saturated silica nanochannels. Remarkably, up to 80\% reduction in the effective permeability is found for water-driven heptane flow in a baseline 4.5 nm-wide slit channel, when compared with the Hagen–Poiseuille equation. In the 4.5 nm-wide channels with architected surface roughness, the flow rate is found either higher or lower than the baseline case, depending on the shape and size of cross-sections. High wettability of the solid surface to water is essential for achieving high recovery of heptane, regardless of surface roughness. When the solid surface is less wetting or non-wetting to water, the existence of an optimal waterflooding driven power is found to allow for the highest possible recovery. A detailed analysis on the evolution of the transient water-heptane interface in those nanochannels is presented to elucidate the underlying mechanisms that impact or dictate the multiphase flow behaviors.

58 GEOSCIENCES↗
NASA NTRS1993

Direct simulation of isothermal-wall supersonic channel flow

The motivation for this work is the fact that in turbulent flows where compressibility effects are important, they are often poorly understood. A few examples of such flows are those associated with astrophysical phenomena and those found in combustion chambers, supersonic diffusers and nozzles, and over high-speed airfoils. For this project, we are primarily interested in compressibility effects near solid surfaces. Our main objective is an improved understanding of the fundamentals of compressible wall-bounded turbulence, which can in turn be used to cast light upon modeling concepts such as the Morkovin hypothesis and the Van Driest transformation. To this end, we have performed a direct numerical simulation (DNS) study of supersonic turbulent flow in a plane channel with constant-temperature walls. All of the relevant spatial and temporal scales are resolved so that no sub grid scale or turbulence model is necessary. The channel geometry was chosen so that finite Mach number effects can be isolated by comparing the present results to well established incompressible channel data. Here the fluid is assumed to be an ideal gas with constant specific heats, constant Prandtl number, and power-law temperature-dependent viscosity. Isothermal-wall boundary conditions are imposed so that a statistically stationary state may be obtained. The flow is driven by a uniform (in space) body force (rather than a mean pressure gradient) to preserve stream wise homogeneity, with the body force defined so that the total mass flux is constant.

Coleman, Gary N.↗
NASA NTRS2011

Local Leak Detection and Health Monitoring of Pressurized Tanks

An optical gas-detection sensor safely monitors pressurized systems (such as cryogenic tanks) and distribution systems for leaks. This sensor system is a fiber-coupled, solid optical body interferometer that allows for the miniaturized sensing element of the device to be placed in the smallest of recesses, and measures a wide range of gas species and densities (leaks). The deflection of the fringe pattern is detected and recorded to yield the time-varying gas density in the gap. This technology can be used by manufacturers or storage facilities with toxic, hazardous, or explosive gases. The approach is to monitor the change in the index of refraction associated with low-level gas leaks into a vacuum environment. The completion of this work will provide NASA with an enabling capability to detect gas system leaks in space, and to verify that pressurized systems are in a safe (i.e. non-leaking) condition during manned docking and transit operations. By recording the output of the sensor, a time-history of the leak can be constructed to indicate its severity. Project risk is mitigated by having several interferometric geometries and detection techniques available, each potentially leveraging hardware and lessons learned to enhance detectability.

Polzin, Kurt↗
NASA NTRSDate not supplied

Center for Isotope Cosmochemistry and Geochronology at NASA Johnson Space Center

The Center for Isotope Cosmochemistry and Geochronology (CICG) at the NASA Johnson Space Center, led by Justin Simon, applies novel elemental and isotopic analytical techniques to understand solar system formation, from processes occurring during the earliest Solar Nebula, to the evolution of planetary bodies during planet formation and differentiation. CICG consists of an integrated suite of labs including dedicated spaces for specialized handling and preparation of meteorites and planetary return samples, metal-free clean chemistry, and mass spectrometry. In addition to a Thermo-Scientific Triton thermal ionization mass spectrometer (TIMS) for high precision isotopic analyses in processed samples, CICG houses two additional mass spectrometry instruments with added laser ablation capabilities for high spatial resolution in-situ measurements: (1) a Thermo-Scientific Element-XR inductively coupled mass spectrometer (ICP-MS) with a Teledyne Photon Machines Analyte Excite+ laser ablation system, and (2) a Nu Sapphire 1700 multi-collector ICP-MS, a large geometry instrument with a multi-pole collision cell for interference removal, with an Applied Spectra J200 tandem laser induced breakdown spectroscopy (LIBS) femto-second laser ablation system. We detail the analytical capabilities of CICG in the context of current and future research projects related to the studies of protoplanetary disk evolution via early-formed Solar System solids, volatile abundances and distribution within the inner solar system, and geochemical and temporal investigations related to planet formation and differentiation. We also highlight opportunities through the relationship with the Center of the Advancement of Space Safety and Mission Assurance Research (CASSMAR) at the University of Texas at El Paso. CICG is open to collaborations and participates in the NASA Facility for Astromaterials Research (NFAR) program, which accepts research proposals for access to the unique facility capabilities.

Jacob B. Setera↗
NASA NTRS1997

Reactive Hydrodynamics in Rotating Spherical and Cylindrical Geometry

In turbulent spray combustion among many complex interactions between local flow structures called turbulent eddies and droplets are those associated with rotation of droplets. In general, for a complete statistical description of turbulent sprays, consideration of at least four degrees of freedom respectively associated with translational, rotational, vibrational (pulsational), and internal motions of the droplet are needed. Clearly the interactions between all degrees of freedom of the droplets and those for the gaseous background field will be exceedingly complex. For example, one type of interaction between the translational and the rotational velocity of droplets results in droplet helicity, H(d) = w(d).v(d), the significance of which in turbulent spray combustion is yet to be recognized. The role of droplet rotation in turbulent spray combustion modeling and its impact on the evaporation of liquid fuel droplets was recently investigated. Also, the impact of rotation on combustion of solid particles such as is encountered in pulverized coal combustion has been emphasized. The problem of viscous flow around a rotating sphere discussed above also occurs in other areas of physical sciences such as astrophysics and geophysics. Consequently, the subject has been addressed in many classical as well as more recent investigations. According to these investigations, the rotation of a rigid sphere in an otherwise quiescent, unconfined environment results in the motion of the fluid towards the poles. The polar flows from the northern and southern hemispheres move along helical trajectories towards the equatorial plane. Eventually, the polar flows collide at the equatorial plane, thus producing a sheet of rotating fluid that is radially ejected outward on this plane. Therefore, a droplet induces a strained flow field as a result of its rotation. Since the spatial extent of equatorial jets could easily exceed many droplet diameters, interactions between neighboring droplets are enhanced as a result of their rotation. Also, the equatorial jet substantially alters the spherical geometry of the diffusion flame surface that surrounds a rotating droplet. The objective of the research is to gain more knowledge about the hydrodynamics within and around rotating spherical and cylindrical body of fluid, and the behavior of diffusion or premixed flame surfaces that could surround such symmetric body of rotating fluid.

Sohrab, Siavash H.↗
NASA NTRS1995

Temperature and Forward Flight Effects on an Underexpanded Jet Noise

An experimental investigation was carried out in the NASA Ames 40'x80' wind tunnel to investigate the far field noise characteristics of a supersonic jet exiting from an axisymmetric convergent nozzle. The nozzle geometry conforms to the ASME specifications for the long radius nozzle. The nozzle pressure ratio NPR, (stagnation pressure/ambient pressure) was varied from 2.5 to 4.5. The temperature ratio, TR (stagnation temperature/ambient temperature) was varied from 2.5 to 3.5. The resulting Reynolds number range, based on the nozzle exit diameter of 12.55cm, is from 0.98 -1.27 x 10(exp 6). The far field directivity was obtained using 1/4 inch diameter condenser microphone with the Flow-Induced Tone Eliminator (FITE) aerodynamic microphone fore body. Appropriate microphone corrections, developed by Allen et al., were made for accurate in-flow acoustic measurements at high frequencies. The narrow band (band width = 64 Hz) frequency spectra covering a range from 0 to 70 KHz were obtained with an accuracy of plus or minus 0.6dB. Typical narrow-band spectra representing far field noise in the forward quadrant, normal to the jet axis and the aft quadrant are shown. The three dominant components of the noise, the screech tone, broad-band shock associated noise and the mixing noise, are clearly identifiable in the spectra. The variation of the screech tone with the exit Mach number (calculated from the isentropic equation relating NPR to Mach number), is shown. The solid line in the figure is calculated using Tam's formula. The model developed by Tam seems to work well even at high temperatures. The appearance of the screech tone is confined to the forward quadrant (theta less than or equal to 60 degrees; the angle theta is measured from the inlet axis). The amplitude of the tone is about 10 dB higher than the broad band noise, which is less than the normally observed amplitude for cold jets operating at similar conditions.

Krothapalli, A.↗
DOE OSTI2023

NbV_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-vanadium (Nb-V). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and V. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2023

NbTa_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-tantalum (Nb-Ta). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb. For Ta, p semi-core states as valence were chosen. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for ternary alloys is AXBYCZ, where A, B, and C refer to the constituents, and X, Y, and Z are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2023

TaV_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-vanadium (Tanu-V). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the element V. For Ta, p semi-core states as valence were chosen. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name case-N where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. Subdirectories with name case-N, where N ranges between 1 and 10 (extremes included) contain all the files listed above except 'vaspout.h5'. Subdirectories with name case-N, where N ranges between 41 and 60 (extremes included), contain a duplicate copy of the files listed above except for KPOINTS. The names of the duplicate files end with -bis, and correspond to a second VASP calculation that has converged to a different optimized geometry. This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. and Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. and Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. and Furthmuller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15-50 (1996) (4) Kresse, G. and Furthmuller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. and Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

NbZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-zirconium (Nb-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

TaZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-zirconium (Ta-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Ta and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 80, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10 and between 81 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
NASA NTRS2014

The Surface Compositions of Pluto and Charon

The surface of Pluto as it is understood on the eve of the encounter of the New Horizons spacecraft (mid-2015) consists of a spatially heterogeneous mix of solid N2, CH4, CO, C2H6, and an additional component that imparts color, and may not be an ice. The known molecular ices are detected by near-infrared spectroscopy. The N2 ice occurs in the hexagonal crystalline b-phase, stable at T greater than 35.6 K. Spectroscopic evidence for wavelength shifts in the CH4 bands attests to the complex mixing of CH4 and N2 in the solid state, in accordance with the phase diagram for N2 + CH4. Spectra obtained at several aspects of Plutos surface as the planet rotates over its 6.4-day period show variability in the distribution of CH4 and N2 ices, with stronger CH4 absorption bands associated with regions of higher albedo, in correlation with the visible rotational light curve. CO and N2 ice absorptions are also strongly modulated by the rotation period; the bands are strongest on the anti-Charon hemisphere of Pluto. Longer term changes in the strengths of Plutos absorption bands occur as the viewing geometry changes on seasonal time-scales, although a complete cycle has not been observed. The non-ice component of Plutos surface may be a relatively refractory material produced by the UV and cosmic-ray irradiation of the surface ices and gases in the atmosphere, although UV does not generally penetrate the atmospheric CH4 to interact with the surface. Laboratory simulations indicate that a rich chemistry ensues by the irradiation of mixtures of the ices known to occur on Pluto, but specific compounds have not yet been identified in spectra of the planet. Charons surface is characterized by spectral bands of crystalline H2O ice, and a band attributed to one or more hydrates of NH3. Amorphous H2O ice may also be present; the balance between the amorphization and crystallization processes on Charon remains to be clarified. The albedo of Charon and its generally spatially uniform neutral color indicate that a component, not yet identified, is mixed in some way with the H2O and NH3nH2O ices. Among the many known small bodies in the transneptunian region, several share characteristics with Pluto and Charon, including the presence of CH4, N2, C2H6, H2O ices, as well as components that yield a wide variety of surface albedo and color. The New Horizons investigation of the Pluto-Charon system will generate new insight into the physical properties of the broader transneptunian population, and eventually to the corresponding bodies expected in the numerous planetary systems currently being discovered elsewhere in the Galaxy.

surface composition↗
DOE OSTI2022

Glassy quantum nuclear pasta in neutron star crusts

We conduct a comprehensive survey of the shape parameter space of the nuclear pasta phases in neutron star crusts by conducting three-dimensional Hartree-Fock + BCS calculations. Spaghetti, waffles, lasagna, bicontinuous phases and cylindrical holes occupy local minima in the resulting constant-pressure Gibbs energy surfaces, implying multiple geometries coexist at a given depth. Notably, the bicontinuous phase, in which both the neutron gas and nuclear matter extend continuously in all dimensions appears over a large depth range. Our results support the idea that nuclear pasta is a glassy system. At a characteristic temperature, of order 10 8 – 10 9 K, different phases may become frozen into domains whose sizes we estimate to be 1–50 times the lattice spacing and over which the local density and electron fraction can vary. Above this temperature, very little long-range order exists and matter is an amorphous solid. Electron scattering off domain boundaries may contribute to the disorder resistivity of the pasta phases. Annealing of the domains may occur during cooling; repopulating of local minima during crustal heating might lead to temperature-dependent transport properties in the deep crust layers. We identify four regions distinguished by whether pasta is the true ground state, and whether the pasta structure allows delocalization of protons. The whole pasta region can occupy up to 70% of the crust by mass and 25% by thickness, and the layer in which protons are delocalized could occupy 45% of the crust mass and 15% of its thickness.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A sharp interface Lagrangian-Eulerian method for flexible-body fluid-structure interaction

This paper introduces a sharp-interface approach to simulating fluid-structure interaction (FSI) involving flexible bodies described by general nonlinear material models and across a broad range of mass density ratios. This new flexible-body immersed Lagrangian-Eulerian (ILE) scheme extends our prior work on integrating partitioned and immersed approaches to rigid-body FSI. Our numerical approach incorporates the geometrical and domain solution flexibility of the immersed boundary (IB) method with an accuracy comparable to body-fitted approaches that sharply resolve flows and stresses up to the fluid-structure interface. Unlike many IB methods, our ILE formulation uses distinct momentum equations for the fluid and solid subregions with a Dirichlet-Neumann coupling strategy that connects fluid and solid subproblems through simple interface conditions. As in earlier work, we use approximate Lagrange multiplier forces to treat the kinematic interface conditions along the fluid-structure interface. This penalty approach simplifies the linear solvers needed by our formulation by introducing two representations of the fluid-structure interface, one that moves with the fluid and another that moves with the structure, that are connected by stiff springs. This approach also enables the use of multi-rate time stepping, which allows us to use different time step sizes for the fluid and structure subproblems. Our fluid solver relies on an immersed interface method (IIM) for discrete surfaces to impose stress jump conditions along complex interfaces while enabling the use of fast structured-grid solvers for the incompressible Navier-Stokes equations. The dynamics of the volumetric structural mesh are determined using a standard finite element approach to large-deformation nonlinear elasticity via a nearly incompressible solid mechanics formulation. This formulation also readily accommodates compressible structures with a constant total volume, and it can handle fully compressible solid structures for cases in which at least part of the solid boundary does not contact the incompressible fluid. Selected grid convergence studies demonstrate second-order convergence in volume conservation and in the pointwise discrepancies between corresponding positions of the two interface representations as well as between first and second-order convergence in the structural displacements. The time stepping scheme is also demonstrated to yield second-order convergence. To assess and validate the robustness and accuracy of the new algorithm, comparisons are made with computational and experimental FSI benchmarks. Test cases include both smooth and sharp geometries in various flow conditions. Furthermore, we also demonstrate the capabilities of this methodology by applying it to model the transport and capture of a geometrically realistic, deformable blood clot in an inferior vena cava filter.

97 MATHEMATICS AND COMPUTING↗