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ZPR-3 Assembly 6F : A spherical assembly of highly enriched uranium, depleted uranium, aluminum and steel with an average {sup 235}U enrichment of 47 atom %.

Over a period of 30 years, more than a hundred Zero Power Reactor (ZPR) critical assemblies were constructed at Argonne National Laboratory. The ZPR facilities, ZPR-3, ZPR-6, ZPR-9 and ZPPR, were all fast critical assembly facilities. The ZPR critical assemblies were constructed to support fast reactor development, but data from some of these assemblies are also well suited for nuclear data validation and to form the basis for criticality safety benchmarks. A number of the Argonne ZPR/ZPPR critical assemblies have been evaluated as ICSBEP and IRPhEP benchmarks. Of the three classes of ZPR assemblies, engineering mockups, engineering benchmarks and physics benchmarks, the last group tends to be most useful for criticality safety. Because physics benchmarks were designed to test fast reactor physics data and methods, they were as simple as possible in geometry and composition. The principal fissile species was {sup 235}U or {sup 239}Pu. Fuel enrichments ranged from 9% to 95%. Often there were only one or two main core diluent materials, such as aluminum, graphite, iron, sodium or stainless steel. The cores were reflected (and insulated from room return effects) by one or two layers of materials such as depleted uranium, lead or stainless steel. Despite their more complex nature, a small number of assemblies from the other two classes would make useful criticality safety benchmarks because they have features related to criticality safety issues, such as reflection by soil-like material. ZPR-3 Assembly 6 consisted of six phases, A through F. In each phase a critical configuration was constructed to simulate a very simple shape such as a slab, cylinder or sphere that could be analyzed with the limited analytical tools available in the 1950s. In each case the configuration consisted of a core region of metal plates surrounded by a thick depleted uranium metal reflector. The average compositions of the core configurations were essentially identical in phases A - F. ZPR-3 Assembly 6F (ZPR-3/6F), the final phase of the Assembly 6 program, simulated a spherical core with a thick depleted uranium reflector. ZPR-3/6F was designed as a fast reactor physics benchmark experiment with an average core {sup 235}U enrichment of approximately 47 at.%. Approximately 81.4% of the total fissions in this assembly occur above 100 keV, approximately 18.6% occur below 100 keV, and essentially none below 0.625 eV - thus the classification as a 'fast' assembly. This assembly is Fast Reactor Benchmark No. 7 in the Cross Section Evaluation Working Group (CSEWG) Benchmark Specifications and has historically been used as a data validation benchmark assembly. Loading of ZPR-3/6F began in late December 1956, and the experimental measurements were performed in January 1957. The core consisted of highly enriched uranium (HEU) plates, depleted uranium plates, perforated aluminum plates and stainless steel plates loaded into aluminum drawers, which were inserted into the central square stainless steel tubes of a 31 x 31 matrix on a split table machine. The core unit cell consisted of three columns of 0.125 in.-wide (3.175 mm) HEU plates, three columns of 0.125 in.-wide depleted uranium plates, nine columns of 0.125 in.-wide perforated aluminum plates and one column of stainless steel plates. The maximum length of each column of core material in a drawer was 9 in. (228.6 mm). Because of the goal to produce an approximately spherical core, core fuel and diluent column lengths generally varied between adjacent drawers and frequently within an individual drawer. The axial reflector consisted of depleted uranium plates and blocks loaded in the available space in the front (core) drawers, with the remainder loaded into back drawers behind the front drawers. The radial reflector consisted of blocks of depleted uranium loaded directly into the matrix tubes. The assembly geometry approximated a reflected sphere as closely as the square matrix tubes, the drawers and the shapes of fuel and diluent plates allowed. According to the logbook and loading records for ZPR-3/6F, the reference critical configuration was loading 5 which was critical on January 4, 1957. The subsequent loadings were very similar but were less clean for criticality because there were modifications made to accommodate reactor physics measurements other than criticality. Accordingly, ZPR-3/6F loading 5 was selected as the only configuration for this benchmark. As documented below, it was determined to be acceptable as a criticality safety benchmark experiment. A very accurate transformation to a simplified model is needed to make any ZPR assembly a practical criticality-safety benchmark. There is simply too much geometric detail in an exact (as-built) model of a ZPR assembly. This is especially true of ZPR-3/6F because of the complex core loading required to approximate a sphere with rectangular plates in a square matrix.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Nuclear heating measurements by in-pile calorimetry: prospective works for a microsensor design

Since 2009 works have been performed in the framework of joint research programs between CEA and Aix-Marseille University. The main aim of these programs is to design and develop in-pile instrumentations, advanced calibration procedure and accurate measurement methods in particular for the new Material Testing Reactor (MTR) under construction in the South of France: Jules Horowitz Reactor (JHR). One major sensor is a specific radiometric calorimeter, which was studied out-of-pile from a thermal point of view and in-pile during irradiation campaigns. This sensor type is dedicated to measurements of nuclear heating (energy deposition rate per mass unit induced by interactions between nuclear rays and matter) inside experimental channels of MTRs. This kind of in-pile calorimeter corresponds to heat flux calorimeter exchanging with the external cooling fluid. This thermal running mode allows the establishment of steady thermal conditions inside the sensor to carry out online continuous measurements inside the reactor (core or reflector). Two main types of calorimeters exist. The first type consists of a single cell calorimeter. It is divided into a sample of material to be tested and a jacket instrumented with two thermocouples or a single thermocouple (Gamma Thermometer). The second, called a differential calorimeter, is composed of two superposed twin cells (a measurement cell containing a sample of material, and a reference cell to remove the heating of the cell body) instrumented with four thermocouples and two electrical heaters. Contrary to a single-cell calorimeter, a differential calorimeter allows the compensation of the parasite nuclear heating of the sensor body or jacket. Moreover, it possesses interesting advantages: thanks to the heaters embedded in the cells, three different measurement methods can be applied during irradiations to quantify nuclear heating. The first one is based on the use of out-of-pile calibration curves obtained by generating a heat source by the Joule Effect inside each calorimetric cell. The second one is a zero method consisting in cancelling the difference in cell responses with an additional energy into the reference cell. The last measurement method is based on current additions in the two calorimetric cells. However, one drawback of the existing differential calorimeter is the size of the sensor: a great length equal to 220 mm and a diameter equal to 18 mm. This current size leads to measurement limitations. This paper will begin with a presentation of these measurement limitations from a bibliographic state. Each limitation will be detailed and in particular in the case of a high nuclear heating level expected, for instance, inside the JHR's core at its highest nominal power. The second part of the paper will develop the scientific skills of each partner in heat sciences, micro technology and nuclear physics necessary to design a new calorimetric micro-system: the advantages of studied microelements such as micro-thermocouples, micro- fluxmeters and micro-heaters will be presented. The last part will discuss preliminary designs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Kinetic Monte Carlo Simulation of Hydrogen Diffusion in Tungsten

Nuclear fusion is considered as the most promising sustainable energy source. If achieved, it could provide electricity to the mankind for millions of years. During the reaction, the fusion ash is continuously removed from the magnetic gap, the divertor region, and the divertor facing component is subjected to intense plasma bombardment. It is proposed to use tungsten as the divertor facing material, because it has low physical sputtering yield and superior thermomechanical properties. However, the high mobility of hydrogen in tungsten and the high bombardment flux bring the hydrogen isotopes deep into the tungsten tile. The presence of hydrogen in tungsten tends to weaken the gram boundaries and produce surface blistering. The ejection of tungsten particulates into the plasma core will cool the reaction and cease the fusion reactions. Therefore, it is important to have a detailed understanding of the hydrogen diffusion in tungsten bulk. The KMC method is a stochastic computer simulation to predict the time evolution of a certain system. Typically, at a given time, the system will evolve through a few possible paths with known probabilities. In each step, the KMC method analyzes the entire system and randomly determines the system evolving events based on all transition rates of all possible events and advance the physical time. This process is repeated till the simulation of the entire physical time is completed. In previous work, the authors developed a kinetic Monte Carlo (KMC) algorithm and program to simulate hydrogen diffusion process on tungsten reconstructed surface. In this article, this KMC method is extended to model the hydrogen diffusion in tungsten bulk. The diffusion coefficients are calculated and fitted to the Arrhenius equation. The result matches the experiment measurement very well. KMC diffusion simulation needs the locations of the hydrogen trapping sites, diffusion paths and the corresponding diffusion energy barriers. The hydrogen interstitial sites in tungsten crystal have been thoroughly studied by density function theory (DFT) and molecular dynamics (MD). Johnson and Carter used DFT to determine the preferred location of the hydrogen interstitial sites m tungsten crystal, which are the tetrahedral sites (T-sites) in body-centered-cubic (bcc) lattice surfaces. All T-sites are located on the surfaces of a lattice cell. There are four T-sites per surface and twelve T-sites per lattice cell. The hydrogen atoms diffuse by traveling from one T-site to one of its nearest four T-site neighbors. Thus, each T-site is surrounded by two neighbors on the same surface, and one neighbor on each of the two adjacent surfaces perpendicular to the surface holding the center T-site. The diffusion barrier between two neighboring T-sites was calculated to be 0.38 eV. Knowing the diffusion path, energy barrier, and the crystal structure, the KMC simulation is readily to be performed. Based on other researchers' DFT calculations, the hydrogen stable interstitial site in tungsten bulk is determined as the tetrahedral site on the surface of the bee lattice. Each T-site has 4 neighbors and the energy barrier between two adjacent T-sites are estimated to be 0.38 eV. Based on those facts and the KMC theory, a MATLAB program is developed to model the hydrogen diffusion in tungsten bulk. In the MATLAB program, substrate temperature, hydrogen concentration, physical time to be simulated, and the number of cycles are all customizable. To compare the KMC determined diffusion coefficients with experiment results in the literature, the simulations are performed for temperature 300 K - 2500 K and low hydrogen concentration (0.1%), mimicking the experiment condition. The calculated diffusion coefficients match the reference values measured by the experiment under low hydrogen concentration condition. The results are also fitted to the Arrhenius equation. The pre-exponential factor and the average activation energy in Arrhenius equation are obtained. The good agreement between simulation and experiment proves that the developed KMC algorithm truly reflect the well-established theory of interstitial hydrogens in tungsten. The developed program could easily be used to model the diffusion process and predict the diffusion coefficients under other concentrations and temperatures. This work is part of the ongoing development of hydrogen diffusion in tungsten integrating surface diffusion (previous work), bulk diffusion (this work), and adsorption/desorption on surface (future work)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Report on Project performance under the CRADA No PNNL/254, PLA 1, between BATTELLE as operator of Pacific Northwest National Laboratory under its U.S. Department of Energy Contract No. DE-AC05-76RL0 1830 and Isonics Corporation, Chairman and CEO: Christopher M. Toffales

Unattended decontamination of bio agents in enclosed environments. The goal of the project is the development of unattended method for environmentally safe, highly effective volumetric decontamination that kills on a contact multiple bio-agents including airborne and surface-adsorbed pathogens of different nature in enclosed environments with complex geometry like various buildings and transportation units as aircraft and ships The method employs two elements, proprietary Vortical Aerosol Generator (VAG) and Electrochemically Activated Solutions (EAS). The prototype of the method was developed under CRADA No PNNL/254, and was shown highly effective for irreversible decontamination of vegetative microbial cells, spores and viruses in a test chamber of ~100 ft 3 and in a room of ~3000 ft 3 . The electrochemical activation technology is based on the production of meta-stable (activated) solutions with anomalous physicochemical and catalytic reactivity for their further use in various technological processes in place of traditionally applied aggressive chemicals. Usual source for electrochemical activation processes is sodium chloride aqueous solution with the concentration of 1.0 g/L. EAS bactericidal effectiveness was previously demonstrated for different applications however, in all previous applications EAS was only used as a liquid agent. Present Method suggests an application of EAS as micro-aerosol (particle size in the range of 0.5-10 mµ), which behaves like a gas and eventually fills entire enclosed environment including hardly, not otherwise accessible sites. The term micro-aerosol of EAS (MAEAS) herein refers to an air-suspended EAS having micro-particle droplets of characteristic cross section and/or size comprising electrochemically-active agents with sufficiently large surface areas whereby the probability of a contact with bio agents in a process area is extremely high. EAS is produced with a commercially available STEL device (FEM-3 flow electrochemical module element; US Pat 5,635,040). Pneumatic Vortical Aerosol Generator (VAG) produces MAEAS. VAG is an original high output atomizer allowing for the production of the aerosol particles with the size in the range of 0.5 to 12 µm at the rate from 5 to 300 ml/min correspondingly, allowing a control of a required particle size. MAEAS has significant advantages over EAS, allowing for simultaneous decontamination of airborne and surface-adsorbed bio agents with minimal volume of atomized EAS and with essentially no waste. Experimental data indicate that MAEAS has very low corrosiveness, possess no negative effect to sensitive equipment and does not destroy the surface of different tested materials. Limited toxicity tests were conducted with mice directly exposed to EAS aerosol; no acute and short-term toxicity was identified.

bioagents↗

Assessment of human nuclear and mitochondrial DNA qPCR assays for quantification accuracy utilizing NIST SRM 2372a

In forensic DNA casework, a highly accurate real-time quantitative polymerase chain reaction (qPCR) assay is recommended per the Scientific Working Group on DNA Analysis Methods (SWGDAM) (SWGDAM Validation Guidelines for DNA Analysis Methods [1]) to determine whether a DNA sample is of sufficient quantity and robust quality to move forward with downstream short tandem repeats (STR) or sequencing analyses. Most of these assays rely on a standard curve, referred to herein and traditionally as absolute qPCR, in which an unknown is compared, relative to that curve. However, one fundamental issue with absolute qPCR is the quantifiable concentration of commercial assay standards can vary depending on (1) origin, i.e., whether from a cell line or a human subject, (2) supplier, (3) lot number, (4) shipping method, etc. In 2018, the National Institute for Standards and Technology (NIST) released a human DNA standard reference material for evaluating qPCR quantification standards, Standard Reference Material (SRM) 2372a, Romsos et al. (2018) [2] which contains three well-characterized human genomic DNA samples: Component A) a single male1 donor, Component B) a single female 1 donor, and Component C) a 1:3 male 2 :female 2 donor, each with certification data for nDNA and informational mitochondrial DNA(mtDNA)/nuclear DNA (nDNA) ratio data. The SRM 2372a was used to assess four qPCR assays: (1) Quantifiler Trio (Thermo Fisher Scientific, Waltham, MA) for nDNA quantification, (2) NovaQUANT (EMD Millipore Corporation, San Diego, CA) for nDNA and mtDNA quantification, (3) a custom duplex mtDNA assay, and (4) a custom triplex mtDNA assay. Additionally, extracts from eighteen (18) skeletal remains were tested with the latter three assays for concordance of DNA concentration and with assays (2) and (3), for the degradation state. Our assessment revealed that an accurate, efficient, and reproducible qPCR assay is dependent on (1) the quality and reliability of the DNA standard, (2) the qPCR chemistry, and (3) the specific primers, and probes (if applicable), used in an assay. Finally, our findings indicate qPCR assays may not always quantify as expected and that performance of each lot should be verified using a well-characterized DNA standard such as the NIST SRM 2372a and adjusted if warranted.

59 BASIC BIOLOGICAL SCIENCES↗

Renewed Search for FUN (Fractionated and Unidentified Nuclear Effects) in Primitive Chondrites

Ca-Al-rich inclusions (CAIs) found in primitive chondrites record processes and conditions of the earliest solar system as they are the oldest known solid objects formed in the solar system [1,2]. CAIs with fractionation and unidentified nuclear anomalies (FUN CAIs; [3]) are very rare and thusfar found exclusively in CV carbonaceous chondrites (e.g., Allende and Vigarano)[4]. FUN CAIs are characterized by large nucleosynthetic anomalies in several elements (Ca, Ti, Si, Sr, Ba, Nd, and Sm), large mass-dependant isotope fractionation (Mg, Si, and O), and very little initial {sup 26}Al [4,5 and reference therein]. Formation of FUN CAIs by thermal processing of presolar dust aggregates prior to the injection of {sup 26}Al into the protoplanetary disk has been proposed. More recently [5] proposed that FUN CAIs formed from a protosolar molecular cloud after injection of {sup 26}Al but before {sup 26}Al and {sup 27}Al were completely homogenized. Therefore discovering more FUN CAIs to perform U-Pb and other short-lived chronometric dating will provide key constraints on the age of the solar system, the isotopic composition of the protosolar molecular cloud, the earliest stages of the thermal processing in the solar system and the timing of {sup 26}Al and other short-lived radionuclide injection into the nascent solar system. Most known FUN CAIs were discovered and studied > 30 yr ago, and their isotope ratios determined using thermal ionization mass spectrometry (TIMS). Most of these FUN CAIs were almost or entirely consumed during their respective analyses. [5] recently identified a new FUN CAI (NWA 779 KS-1) based on O and Mg isotope ratios determined by SIMS and MCICPMS, respectively. We have initiated a systematic search for FUN CAIs in primitive chondrites, taking advantage of the large mass-dependant Mg isotope effects known for FUN inclusions with little or no inferred {sup 26}Al. Our strategy is to use newly developed sample cells capable of holding very large slabs of meteorites for laser ablation interfaced with a multicollector inductively coupled plasma mass spectrometry (LA-MC-ICPMS) at UC Davis. Here report the initial findings of our search and describe the instrument setup we use that provides rapid throughput and accurate results.

79 ASTRONOMY AND ASTROPHYSICS↗

A grid-connected photovoltaic power conversion system with single-phase multilevel inverter

This paper presents a grid-connected photovoltaic (PV) power conversion system based on a single-phase multilevel inverter. The proposed system fundamentally consists of PV arrays and a single-phase multilevel inverter structure. First, configuration and structural parts of the PV assisted inverter system are introduced in detail. To produce reference output voltage waves, a simple switching strategy based on calculating switching angles is improved. By calculated switching angles, the reference signal is produced as a multilevel shaped output voltage wave. The control algorithm and operational principles of the proposed system are explained. Operating PV arrays in the same load condition is a considerable point; therefore a simulation study is performed to arrange the PV arrays. After determining the number and connection types of the PV arrays, the system is configured through the arrangement of the PV arrays. The validity of the proposed system is verified through simulations and experimental study. The results demonstrate that the system can achieve lower total harmonic distortion (THD) on the output voltage and load current, and it is capable of operating synchronous and transferring power values having different characteristic to the grid. Hence, it is suitable to use the proposed configuration as a PV power conversion system in various applications. (author)

14 SOLAR ENERGY↗

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↗

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↗

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↗

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↗

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↗

Review of the U.S. Department of Energy's "Deep Dive" Effort to Understand Voltage Fade in Li- and Mn-rich Cathodes

With roughly two-thirds of daily oil consumption in the United States allotted for transportation, the possibility of efficient and affordable electric vehicles suggests a way to substantially alleviate the Country’s dependence on oil and mitigate the rise of greenhouse gases. Although commercialized Li-ion batteries do not currently meet the stringent demands of a would-be, economically competitive, electrified vehicle fleet, significant efforts are being focused on promising new materials for the next generation of Li-ion batteries. The leading class of materials most suitable for the challenge is the Li- and manganese-rich class of oxides. Denoted as LMR-NMC (Li-manganese-rich, nickel, manganese, cobalt), these materials could significantly improve energy densities, cost, and safety, relative to state-of-the-art Ni- and Co-rich Li-ion cells, if successfully developed.1 The success or failure of such a development relies heavily on understanding two defining characteristics of LMR-NMC cathodes. The first is a mechanism whereby the average voltage of cells continuously decreases with each successive charge and discharge cycle. This phenomenon, known as voltage fade, decreases the energy output of cells to unacceptable levels too early in cycling. The second characteristic is a pronounced hysteresis, or voltage difference, between charge and discharge cycles. The hysteresis represents not only an energy inefficiency (i.e., energy in vs energy out) but may also complicate the state of charge/depth of discharge management of larger systems, especially when accompanied by voltage fade. n 2012, the United States Department of Energy’s Office of Vehicle Technologies, well aware of the inherent potential of LMR-NMC materials for improving the energy density of automotive energy storage systems, tasked a team of scientists across the National Laboratory Complex to investigate the phenomenon of voltage fade. Unique studies using synchrotron X-ray absorption (XAS) and high-resolution diffraction (HR-XRD) were coupled with nuclear magnetic resonance spectroscopy (NMR), neutron diffraction, high-resolution transmission electron microscopy (HR-TEM), first-principles calculations, molecular dynamics simulations, and detailed electrochemical analyses. These studies demonstrated for the first time the atomic-scale, structure–property relationships that exist between nanoscale inhomogeneities and defects, and the macroscale, electrochemical performance of these layered oxides. These inhomogeneities and defects have been directly correlated with voltage fade and hysteresis, and a model describing these mechanisms has been proposed. This Account gives a brief summary of the findings of this recently concluded, approximately three-year investigation. Lastly, the interested reader is directed to the extensive body of work cited in the given references for a more comprehensive review of the subject.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Electrochemically driven carbon dioxide separation

This project explored the viability of a Ni(OH) 2 based hydroxide exchange membrane carbon capture (HEMCC) device for direct air capture. It is built off an H 2 fuel cell based HEMCC previously developed in the Yushan Yan group at University of Delaware. Taking the same membrane-based, electrochemically driven pH gradient concept, similar flux performance was able to be seen in the Ni(OH) 2 system as the H 2 system when using comparable current densities. The project produced two types of membrane electrode assemblies (MEA). The first was a traditional MEA with two electrodes and a membrane, the second was a flow-through membrane MEA. Consistent performance was achieved with the traditional MEA with an energy cost of 1.1 MWh∙ton -1 and flux of 82 kg∙m -2 ∙yr -1 . This was the most stable of the two designs. This project investigated strategies to improve performance with a flow-through membrane design. Two designs were made, one with a cast phase inversion membrane, and one with powdered membrane layer. The template phase inversion membrane achieved low pressure drop but had performance limitations due to a skin layer of membrane limiting CO 2 gas transport. The powdered membrane layer had better performance but higher pressure drop. Finally, using the powdered membrane, commercial battery materials were able to be used in order to achieve higher flux. This showed that the flow through membrane design does have the capability to overcome flux inefficiencies in the traditional MEA. Moreover, the process design of the system is proposed and given in this project. The process mass and energy balances were calculated for a reference plant of 1000 t/yr CO 2 capture, which contains several subsystems, e.g. air processing subsystem, electrical subsystem, and CO 2 purification and compression subsystem is designed and evaluated. Based on our calculation, When the power consumption for stack is 1 MWh t -1 CO 2 , Additional 382 kWh t -1 CO 2 will be consumed by other subsystem of the plant, i.e. The total power consumption of 1.38 MWh t -1 CO 2 , lower than the final milestone of 1.5 MWh t -1 CO 2 in this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Effect of Curing Temperature and Composition on the Performance Properties of Saltstone - 12473

When freshly mixed Saltstone is placed into a vault at the Savannah River Site, the heat of hydration produced during curing causes the temperature of the grout to increase. It is not uncommon for the temperature of the grout to increase by 30 to 50 deg. C above the starting temperature depending on the vault size and location, environmental conditions and pour schedule. For example, temperatures measured by thermocouples in one cell have exceeded 70 deg. C. The elevated temperatures, once reached, can extend for weeks or even months. Recent studies at Savannah River National Laboratory (SRNL) have shown that curing simulated Saltstone mixes at elevated temperatures can lead to reduced compressive strength and dynamic Young's modulus as well as increased total porosity when compared to the same grout mixes cured at 20 deg. C. Samples cured at 60 deg. C can have hydraulic conductivities approximately 600 times greater than samples cured at 20 deg. C. Previous studies have shown that the 60 deg. C cured mix had a dynamic Young's modulus that was half that of the mix cured at 20 deg. C. However, high temperature curing may not always have negative effects on the performance properties of the grout. These results indicate that higher curing temperatures can significantly alter the performance properties that are inputs for the Saltstone Performance Assessment. The Saltstone formulation (mix design) must produce a grout waste form that meets both placement and performance properties. A statistically designed set of mixes was developed to determine key process and compositional factors that affect the performance properties of Saltstone grout. A total of 27 mixes were batched and tested. There are eight baseline mixes that contain high and low values of aluminate concentration, varying w/p ratio, and varying fly ash content in the premix. Each of the eight mixes was cured at 22, 40 and 60 deg. C. The three additional reference mixes batched at the beginning, middle and end of testing showed good reproducibility throughout the study. The results of varying the operational and compositional factors on the performance properties of Saltstone grout are: - The Young's modulus is inversely affected by the curing temperature of the grout. - The aluminate content of the salt solution increases the Young's modulus of the Saltstone for samples cured at 20 and 40 deg. C. - All grouts cured at 60 deg. C have low Young's moduli and the composition of the grout mix did not have any positive effects on the performance properties after curing at this temperature. - The fly ash content in the premix had not discernible effect on the Young's modulus. - The water to premix ratio does not affect the Young's modulus. - Grouts made with the high aluminate salt solution have lower hydraulic conductivities than those made with the low aluminate simulant. - Temperature has an inverse effect on the hydraulic conductivity. - In general, increasing the water to premix ratio and the fly ash content will increase the hydraulic conductivity. - The porosity and cured density of the Saltstone grout are primarily a factor of the water to premix ratio of the grout formulation. The correlation between hydraulic conductivity and Young's modulus investigated in previous studies was evaluated further in this report. The current data as well as data from previous studies was fitted by a second degree polynomial function with a 77.1 % R-squared value. This model can be used to estimate the hydraulic conductivity of a sample based its Young's modulus for a limited range (E values up to 7 GPa). More data points should be included to further define the relationship between these performance properties are high Young's moduli and low hydraulic conductivities. The Young's modulus should still be measured on grouts formulated in future studies to be used as screening method for samples to be sent for hydraulic conductivity measurements. Saltstone grout has a complicated microstructure that is affected by the composition of the salt solution, the dry feed mixture composition, and the conditions under which it cures. The performance properties investigated are interrelated, which can make it difficult to determine individual relationships between various factors. Porosity is a large factor in determining both the Young's modulus and the hydraulic conductivity of the samples. Although the cured density is a straightforward measurement, it is not a significant performance property that needs further investigation. The porosity measurement provides similar and more meaningful data in relation to other performance properties. Based on the results of this study, the aluminate content of the salt solution does have an effect on the Young's modulus and the hydraulic conductivity and its effects should continue to be investigated. However, a new projection of aluminate contents should be used since projections change over time. The variability of fly ash in the premix did not prove to be a contributing factor that effects the performance properties of Saltstone and does not need to be included in future studies unless significant deviations from the baseline mix are proposed. The water to premix ratio has a significant effect on the performance properties of interest and the effects of varying this factor need further investigation. It is recommended that smaller ranges of w/p are investigated in order to provide data that is more relevant to the Saltstone being placed in the vaults. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Survey of U.S. Research Reactor Auxiliary Facilities Used for Material Testing and Basic Neutron Science

A catalogue of and attributes of the Materials Irradiation and Testing facilities (MIF) and the Basic Neutron Science facilities (BSF) of auxiliary (AUX) facilities of research reactors are compiled. The survey of these facilities is drawn from the set of U.S. university and DOE research reactor facilities and should be considered as a reference point when comparing commonalities in international AUX facilities. The size and shielding capabilities of the facilities, the typical characterization equipment used, and the neutron flux and irradiation capabilities of the facilities are listed. Short descriptions of experimental activities and current practices are detailed for MIFs and BSFs. The attributes are important in consideration of the reconfiguration of the facilities for purposes other than stated mission, i.e., for proliferation of weapons-usable nuclear material (WUNM). An evaluation of the capacity for production rate of WUNM from neutron beams-on-targets or a sample that has been placed in an irradiation position for a period is provided. Further evaluation of reconfiguration of these MIF and BSF are recommended to refine the proliferation risks. Specifically, MIF hot cells and potential configurations, additional modeling of facility throughput and development of methods for determining levels of concern for MIF and BSF have all been identified as steps for refinement of determination of the risks associated with these facilities. Future work will focus on: 1. A model of projected throughput for different MIF and BSF configurations that will be used to provide a window of potential operational misuse for a facility and better understand the capability of production and the rate of processing for WUNM in these facilities. 2. A misuse study for various configurations of MIF hot cell layouts detailing capacity, specifications, shielding constraints for MIF operations, identification of a maximum shielding thickness, and reasonable need associated with each activity performed in a MIF. 3. Development of a rule of thumb rating system utilizing data gathered from previous reports to show levels of concern at a given power level, flux, and experiment set.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Resolution Versus Error for Computational Electron Microscopy

Images that are collected via scanning transmission electron microscopy (STEM) can be undersampled to avoid damage to the specimen while maintaining resolution [1, 2]. We have used BPFA to impute missing data and reduce noise [3]. The reconstruction is typically evaluated using the peak signal-to-noise ratio (PSNR). This measure is too conservative for STEM images and we propose that the Fourier ring correlation (FRC) is used instead to evaluate the reconstruction. We are not concerned with exact reconstruction of the truth image, and therefore PSNR is a conservative estimation of the quality of the reconstruction. Instead, we are concerned with the visual resolution of the image and whether atoms can be distinguished. We have evaluated the reconstruction of a simulated STEM image using the FRC and compared the results with the PSNR measurements. The FRC captures the resolution of the image and is not affected by a large MSE if the atom peaks are still distinguishable. The noisy and reconstructed images are shown in Figure 1. The simulated STEM image was sampled at 100%, 80%, 40%, and 20% of the original pixels to simulate an undersampled scan. The reconstruction was done using BPFA with a patch size of 10 x 10 and no overlapping patches. Not having overlapping patches produces inferior results but they are still acceptable. The dictionary size is 64 and 30 iterations were completed during each reconstruction. The 100% image was denoised instead of reconstructed. Poisson noise was applied to the simulated image with λ values of 500, 50, and 5 to simulate lower imaging dose. The original simulated STEM image was also included in our calculations and was generated using a dose of 1000. The simulated STEM image is 100 by 100 pixels and has essentially no high frequency components. The image reconstruction tends to smooth the data, also resulting in no high frequency components. This causes the FRC of the two images to be large at higher resolutions and may be misleading. For this reason, the BPFA has no overlap to avoid excessive smoothing. Moreover, the resolution of the simulated image is approximately 9.2 (1/nm), so we only look that far in the frequency domain when performing FRC. If the FRC curve does not crossover the threshold, a resolution value of 9.2 is used. We emphasize that our reported results are conservative. The FRC and PSNR values using the ground truth and the reconstructed images are shown in Tables 1 and 2. The left side show the metrics without using BPFA (missing pixels) and the right side show the metrics after using BPFA. When we did not use BPFA, the Fourier transform was estimated [4]. Some threshold curves have been studied [5], but they are derived for additive noise models. Since we have a Poisson noise model, we have used the more conservative threshold of 0.5 for our calculations. Ten images were used to construct each cell of tables in the form of the mean of the metric plus or minus its standard deviation. As expected, the PSNR dies off much quicker than the FRC values for the same image. For the 100% and 80% sampled versions of the truth image, the resolution only dies off when the dose is 5. However, the PSNR dies off rapidly as the dose is reduced. For the 1000, 500, and 50 dose images, the FRC is the maximum, or close, until we undersample at 20%. The PSNR for these values tapers down as we get into the bottom right hand corner of the table, even though the resolution remains high. Overall, we find that undersampled images can be reconstructed to acceptable resolution even when the dose per pixel is also reduced[6]. References: [1]A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [2]A Stevens, L Kovarik, P Abellan et al. Advanced Structural and Chemical Imaging 1(1), (2015), pp. 1. [3]M Zhou, H Chen, J Paisley et al. Image Processing, IEEE Transactions on 21(1), (2012), pp. 130. [4]V. Y. Liepin’sh. Automatic control and computer sciences 30(3), (1996), pp. 20.

Luzi, Lorenzo↗

PERFORMANCE EVALUATION OF A SUSTAINABLE AND ENERGY EFFICIENT RE-ROOFING TECHNOLOGY USING FIELD-TEST DATA

Three test attics were constructed to evaluate a new sustainable method of re-roofing utilizing photo-voltaic (PV) laminates, metal roofing panels, and PCM heat sink in the Envelope Systems Research Apparatus (ESRA) facility in the ORNL campus. Figure 1 is a picture of the three attic roofs located adjacent to each other. The leftmost roof is the conventional shingle roof, followed by the metal panel roof incorporating the cool-roof coating, and third from left is the roof with the PCM. On the PCM roof, the PV panels are seen as well; they're labelled from left-to-right as panels 5, 6 and 7. The metal panel roof consists of three metal panels with the cool-roof coating; in further discussion this is referred to as the infrared reflective (IRR) metal roof. The IRR metal panels reflect the incoming solar radiation and then quickly re-emit the remaining absorbed portion, thereby reducing the solar heat gain of the attic. Surface reflectance of the panels were measured using a Solar Spectrum Reflectometer. In the 0.35-2.0 {mu}m wavelength interval, which accounts for more than 94% of the solar energy, the IRR panels have an average reflectance of 0.303. In the infrared portion of the spectrum, the IRR panel reflectance is 0.633. The PCM roof consists of a layer of macro-encapsulated bio-based PCM at the bottom, followed by a 2-cm thick layer of dense fiberglass insulation with a reflective surface on top, and metal panels with pre-installed PV laminates on top. The PCM has a melting point of 29 C (84.2 F) and total enthalpy between 180 and 190 J/g. The PCM was macro-packaged in between two layers of heavy-duty plastic foil forming arrays of PCM cells. Two air cavities, between PCM cells and above the fiberglass insulation, helped the over-the-deck natural air ventilation. It is anticipated that during summer, this extra ventilation will help in reducing the attic-generated cooling loads. The extra ventilation, in conjunction with the PCM heat sink, are used to minimize thermal stresses due to the PV laminates on sunny days. In PV laminates sunlight is converted into electricity and heat simultaneous. In case of building integrated applications, a relatively high solar absorption of amorphous silicon laminates can be utilized during the winter for solar heating purposes with PCM providing necessary heat storage capacity. However, PV laminates may also generate increased building cooling loads during the summer months. Therefore, in this project, the PCM heat sink was to minimize summer heat gains as well. The PCM-fibreglass-PV assembly and the IRR metal panels are capable of being installed directly on top of existing shingle roofs during re-roofing, precluding the need for recycling or disposal of waste materials. The PV laminates installed on the PCM attic are PVL-144 models from Uni-Solar. Each laminate contains 22 triple junction amorphous silicon solar cells connected in series. The silicon cells are of dimensions 356 mm x 239 mm (14-in. x 9.4-in.). The PVL-144 laminate is encapsulated in durable ETFE (poly-ethylene-co-tetrafluoroethylene) high light-transmissive polymer. Table 1 lists the power, voltage and current ratings of the PVL-144 panel.

14 SOLAR ENERGY↗