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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
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 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↗
DOE OSTI2017

Computational Thermodynamics of Materials Zi-Kui Liu and Yi Wang

This authoritative volume introduces the reader to computational thermodynamics and the use of this approach to the design of material properties by tailoring the chemical composition. The text covers applications of this approach, introduces the relevant computational codes, and offers exercises at the end of each chapter. The book has nine chapters and two appendices that provide background material on computer codes. Chapter 1 covers the first and second laws of thermodynamics, introduces the spinodal as the limit of stability, and presents the Gibbs-Duhem equation. Chapter 2 focuses on the Gibbs energy function. Starting with a homogeneous system with a single phase, the authors proceed to phases with variable compositions, and polymer blends. The discussion includes the contributions of external electric and magnetic fields to the Gibbs energy. Chapter 3 deals with phase equilibria in heterogeneous systems, the Gibbs phase rule, and phase diagrams. Chapter 4 briefly covers experimental measurements of thermodynamic properties used as input for thermodynamic modeling by Calculation of Phase Diagrams (CALPHAD). Chapter 5 discusses the use of density functional theory to obtain thermochemical data and fill gaps where experimental data is missing. The reader is introduced to the Vienna Ab Initio Simulation Package (VASP) for density functional theory and the YPHON code for phonon calculations. Chapter 6 introduces the modeling of Gibbs energy of phases with the CALPHAD method. Chapter 7 deals with chemical reactions and the Ellingham diagram for metal-oxide systems and presents the calculation of the maximum reaction rate from equilibrium thermodynamics. Chapter 8 is devoted to electrochemical reactions and Pourbaix diagrams with application examples. Chapter 9 concludes this volume with the application of a model of multiple microstates to Ce and Fe3Pt. CALPHAD modeling is briefly discussed in the context of genomics of materials. The book introduces basic thermodynamic concepts clearly and directs readers to appropriate references for advanced concepts and details of software implementation. The list of references is quite comprehensive. The authors make liberal use of diagrams to illustrate key concepts. The two Appendices at the end discuss software requirements and the file structure, and present templates for special quasi-random structures. There is also a link to download pre-compiled binary files of the YPHON code for Linux or Microsoft Windows systems. The exercises at the end of the chapters assume that the reader has access to VASP, which is not freeware. Readers without access to this code can work on a limited number of exercises. However, results from other first principles codes can be organized in the YPHON format as explained in the Appendix. This book will serve as an excellent reference on computational thermodynamics and the exercises provided at the end of each chapter make it valuable as a graduate level textbook. Reviewer: Ram Devanathan is Acting Director of Earth Systems Science Division, Pacific Northwest National Laboratory, USA.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Science and Technology Review March 2012

This month's issue has the following articles: (1) Honoring a Legacy of Service to the Nation - The nation pays tribute to George Miller, who retired in December 2011 as the Laboratory's tenth director; (2) Life-Extension Programs Encompass All Our Expertise - Commentary by Bruce T. Goodwin; (3) Extending the Life of an Aging Weapon - Stockpile stewards have begun work on a multiyear effort to extend the service life of the aging W78 warhead by 30 years; (4) Materials by Design - Material microstructures go three-dimensional with improved additive manufacturing techniques developed at Livermore; (5) Friendly Microbes Power Energy-Producing Devices - Livermore researchers are demonstrating how electrogenic bacteria and microbial fuel cell technologies can produce clean, renewable energy and purify water; and (6) Chemical Sensor Is All Wires, No Batteries - Livermore's 'batteryless' nanowire sensor could benefit applications in diverse fields such as homeland security and medicine.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2012

Worker Safety and Security Teams Team Member Handbook

Worker Safety and Security Teams (WSSTs) are an effective way to promote safe workplaces. While WSSTs have a variety of structures and roles, they have one thing in common - employees and management collaborate to find ways to prevent accidents, injuries, and illnesses on the job. The benefits for all concerned are obvious in that employees have a safe place to work, employers save money on lost work time and workers compensation costs, and everyone returns home safe and healthy each day. A successful WSST will have the support and wholehearted participation of management and employees. LANL has a WSST at the institutional level (IWSST) and at all directorates and many divisions. The WSSTs are part of LANL's Voluntary Protection Program (VPP). The WSSTs meet at least monthly and follow an agenda covering topics such as safety shares, behavior based safety (BBS) observations, upcoming events or activities, issues, etc. A WSST can effectively influence safety programs and provide recommendations to managers, who have the resources and authority to implement changes in the workplace. WSSTs are effective because they combine the knowledge, expertise, perspective, enthusiasm, and effort of a variety of employees with diverse backgrounds. Those with experience in a specific job or work area know what the hazards or potential hazards are, and generally have ideas how to go about controlling them. Those who are less familiar with a job or area play a vital role too, by seeing what others may have overlooked or taken for granted. This booklet will cover the structure and operations of WSSTs, what needs to be done in order to be effective and successful, and how you can help, whether you're a WSST member or not.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

RLUOB Celebration

Los Alamos National Laboratory's newest facility, the Radiological Laboratory Utility Office Building (RLUOB), is also its first to achieve both the Leadership in Energy and Environmental Design (LEED) status and LEED Gold certification from the U.S. Green Building Council (USGBC). From its robust design to its advanced scientific equipment, RLUOB is essential to the Laboratory's national security mission in support of the National Nuclear Security Administration's (NNSA) nuclear weapons program. At more than 200,000 square feet, this building is the only radiological facility within the Department of Energy to have attained LEED Gold, which contributes to NNSA's achievement towards the high performance sustainable building goals outlined in Executive Order 13514, Federal Leadership in Environmental, Energy, and Economic Performance. 'As we celebrate RLUOB being completed almost one year ahead of schedule and having achieved LEED certification at the Gold level, we approach our plutonium mission at Los Alamos and NNSA with a great sense of achievement,' said Don Cook, NNSA's deputy administrator for defense programs. 'RLUOB adds a major component to NNSA's plutonium support capability and RLUOB demonstrates our commitment in helping to deliver President Obama's nuclear security agenda which includes ensuring the safety, security and effectiveness of the nuclear deterrent without testing.' The facility contains laboratories for analytical chemistry and materials characterization of special nuclear material, along with space for offices, training and emergency operations. Its multi-functional purpose makes RLUOB a unique project for which LEED certification was sought. 'LEED certification was a huge goal and one we sought from the very beginning of this project,' said Laboratory Director Charlie McMillan. 'It's an important step forward, allowing us to advance national security science in modern, safer, more efficient infrastructure.' The Laboratory's project team and its contractor partners, especially in coordination with Jacobs Engineering, focused on green design and construction in LEED categories, such as sustainable sites, water efficiency, energy and atmosphere, materials and resources, indoor environmental quality and innovation in design. RLUOB's features include: (1) Building envelope design (orientation, materials and insulation) yielded a 20 percent improvement in energy performance; (2) Incorporation of building materials with 24 percent recycled content; (3) Diversion of 72 percent of construction-generated materials through reuse, recycle and salvage; (4) Roofing comprised of 93 percent highly-reflective materials to reduce heat island effects; (5) High efficiency, gas-fired hot water boilers, air-cooled chillers, thermal storage systems and variable frequency drives for compressors, fans and pumps; (6) Energy efficient lighting for interiors, exteriors, process glove boxes and fume hoods; (7) Water efficient fixtures resulting in 30 percent reduction in usage; (8) Low emission paints and carpeting for improved indoor air quality; (9) Landscaping that doesn't require permanent irrigation; (10) Enhanced building system commissioning; and (11) Comprehensive transportation alternatives, including public transportation, bicycle storage and changing rooms, and a refueling station for government vehicles using alternative fuels. 'RLUOB's LEED certification demonstrates tremendous leadership in green building,' says Rick Fedrizzi, president and CEO of USGBC. 'The urgency of our mission has challenged the industry to move faster and reach further than ever before, and RLUOB serves as a prime example of just how much we can accomplish.'

25 ENERGY STORAGE↗
DOE OSTI2012

Fire Protection Program Manual (Revision 3.0)

This manual documents the Lawrence Livermore National Laboratory (LLNL) Fire Protection Program. Department of Energy (DOE) Orders 420.1B, Facility Safety, requires LLNL to have a comprehensive and effective fire protection program that protects LLNL personnel and property, the public and the environment. The manual provides LLNL and its facilities with general information and guidance for meeting DOE 420.1B requirements. The recommended readers for this manual are: fire protection officers, fire protection engineers, fire fighters, facility managers, directorate assurance managers, facility coordinators, and ES and H team members.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Internship at Los Alamos National Laboratory

Los Alamos National Laboratory (LANL) is located in Los Alamos, New Mexico. It provides support for our country's nuclear weapon stockpile as well as many other scientific research projects. I am an Undergraduate Student Intern in the Systems Design and Analysis group within the Nuclear Nonproliferation division of the Global Security directorate at LANL. I have been tasked with data analysis and modeling of particles in a fluidized bed system for the capture of carbon dioxide from power plant flue gas.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2012

Langston University - High Energy Physics (LU-HEP)

This final report is presented by Langston University (LU) for the project entitled "Langston University High Energy Physics" (LUHEP) under the direction of principal investigator (PI) and project director Professor Joel Snow. The project encompassed high energy physics research performed at hadron colliders. The PI is a collaborator on the DZero experiment at Fermi National Accelerator Laboratory in Batavia, IL, USA and the ATLAS experiment at CERN in Geneva, Switzerland and was during the entire project period from April 1, 1999 until May 14, 2012. Both experiments seek to understand the fundamental constituents of the physical universe and the forces that govern their interactions. In 1999 as member of the Online Systems group for Run 2 the PI developed a cross-platform Python-based, Graphical User Interface (GUI) application for monitoring and control of EPICS based devices for control room use. This served as a model for other developers to enhance and build on for further monitoring and control tasks written in Python. Subsequently the PI created and developed a cross-platform C++ GUI utilizing a networked client-server paradigm and based on ROOT, the object oriented analysis framework from CERN. The GUI served as a user interface to the Examine tasks running in the D\O\ control room which monitored the status and integrity of data taking for Run 2. The PI developed the histogram server/control interface to the GUI client for the EXAMINE processes. The histogram server was built from the ROOT framework and was integrated into the D\O\ framework used for online monitoring programs and offline analysis. The PI developed the first implementation of displaying histograms dynamically generated by ROOT in a Web Browser. The PI's work resulted in several talks and papers at international conferences and workshops. The PI established computing software infrastructure at LU and U. Oklahoma (OU) to do analysis of DZero production data and produce simulation data for the experiment. Eventually this included the FNAL SAM data grid system, the SAMGrid (SG) infrastructure, and the Open Science Grid software stacks for computing and storage elements. At the end of 2003 Snow took on the role of global Monte Carlo production coordinator for the DØ experiment. A role which continues til this day. In January of 2004 Snow started working with the SAMGrid development team to help debug, deploy, and integrate SAMGrid with DØ Monte Carlo production. Snow installed and configured SG execution and client sites at LUHEP and OUHEP, and a SG scheduler site at LUHEP. The PI developed a python based GUI (DAJ) that acts as a front end for job submission to SAMGrid. The GUI interfaces to the DZero Mone Carlo (MC) request system that uses SAM to manage MC requests by the physics analysis groups. DAJ significantly simplified SG job submission and was deployed in DZero in an effort to increase the user base of SG. The following year was the advent of SAMGrid job submission to the Open Science Grid (OSG) and LHC Computing Grid (LCG) through a forwarding mechanism. The PI oversaw the integration of these grids into the existing production infrastructure. The PI developed an automatic MC (Automc) request processing system capable of operating without user intervention (other than getting grid credentials), and able to submit to any number of sites on various grids. The system manages production at all but 2 sites. The system was deployed at Fermilab and remains operating there today. The PI's work in distributed computing resulted in several talks at international conferences. UTA, OU, and LU were chosen as the collaborating institutions that form the Southwest Tier 2 Center (SWT2) for ATLAS. During the project period the PI contributed to the online and offline software infrastructure through his work with the Run 2 online group, and played a major role in Monte Carlo production for DZero. During the part of the project period in which the PI served as MC production coordinator MC production increased very significantly. In the first year of the PI's tenure as production coordinator production was 159M events and 6.7~TB of data. During the last year of the project period production was 2,342~M events and 262~TB of data. That is a factor of 15 increase in events and 39 in data volume. The increase occurred with improvements in computer hardware and networks, through the use of grid technology on diverse resources, and through increased automation and efficiency of the production process. LU HEP developed and deployed the automatic MC request processing system in use at FNAL. The complementary strategies of automation and grid production served DZero well. Fermilab has recognized LU HEP's contribution to DZero by allowing the PI to devote full time to research activities by appointing him a guest scientist for the last six years of the project period.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

WildFire Ignition Resistance Estimator (WildFIRE) Wizard Software Development Report

This report describes the development of a software tool, entitled “WildFire Ignition Resistance Estimator Wizard” (WildFIRE Wizard, Version 2.10). This software was developed within the Wildfire Ignition Resistant Home Design (WIRHD) program, sponsored by the U. S. Department of Homeland Security, Science and Technology Directorate, Infrastructure Protection & Disaster Management Division. WildFIRE Wizard is a tool that enables homeowners to take preventive actions that will reduce their home’s vulnerability to wildfire ignition sources (i.e., embers, radiant heat, and direct flame impingement) well in advance of a wildfire event. This report describes the development of the software, its operation, its technical basis and calculations, and steps taken to verify its performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Joint DoD/DOE Munitions Program [Slides]

The JMP was established by an MOU between DOE and DoD in 1985. The JMP is one of three related programs administered by Jose Gonzalez in the Land Warfare and Munitions Office, AT&L. The other two programs are the Joint Fuze and Joint Insensitive Munitions Technology Programs. Oversight is provided by a Technical Advisory Committee (TAC) that is comprised of DoD Laboratory Directors, PMs, PEOs, and senior staff. Each DOE/NNSA weapons laboratory is responsible for identifying the appropriate local matching funds according to their individual needs and mission space.

36 MATERIALS SCIENCE↗
DOE OSTI2012

The Institute of Geophysics and Planetary Physics adds Science of Signatures and a new Deputy to its portfolio

The Institute of Geophysics and Planetary Physics (IGPP) is adding the Science of Signatures to it’s portfolio. It’s new title, the Institute of Geophysics, Planetary Physics and Signatures (IGPPS) is in recognition that Science of Signatures is a LANL strategic science pillar, and is a natural fit with the IGPPS goal of promoting and supporting high quality, cutting-edge science in the areas of astrophysics, space physics, solid planetary geoscience, and climate science. IGPPS Director Dogliani states that, “One cannot fully understand natural phenomena without conducting field measurements of key events.” Beginning in FY13, IGPPS will consider proposals that investigate and develop signature and sensor science for potential use in understanding our evolving climate, geoscience, planetary space and cosmology phenomena.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

2012 Los Alamos National Laboratory Hazardous Waste Minimization Report

Waste minimization and pollution prevention are inherent goals within all the operating procedures of Los Alamos National Security, LLC (LANS). The US Department of Energy (DOE) and LANS are required to submit an annual hazardous waste minimization report to the New Mexico Environment Department (NMED) in accordance with the Los Alamos National Laboratory (LANL or the Laboratory) Hazardous Waste Facility Permit. The report was prepared pursuant to the requirements of Section 2.9 of the LANL Hazardous Waste Facility Permit, which was issued in November 2010. This report describes the hazardous waste minimization program (a component of the overall Waste Minimization/Pollution Prevention [WMin/PP] Program) administered by the Environmental Stewardship Group (ENV-ES). This report also supports the waste minimization and pollution prevention goals of the Environmental Programs Directorate (EP) organizations that are responsible for implementing remediation activities and describes its programs to incorporate waste reduction practices into remediation activities and procedures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Advanced Fuels Campaign 2012 Accomplishments

The Advanced Fuels Campaign (AFC) under the Fuel Cycle Research and Development (FCRD) program is responsible for developing fuels technologies to support the various fuel cycle options defined in the DOE Nuclear Energy Research and Development Roadmap, Report to Congress, April 2010. The fiscal year 2012 (FY 2012) accomplishments are highlighted below. Kemal Pasamehmetoglu is the National Technical Director for AFC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Preparation and Initial Characterization of Fluidized Bed Steam Reforming Pure-Phase Standards

Hanford is investigating the Fluidized Bed Steam Reforming (FBSR) process for their Low Activity Waste. The FBSR process offers a low-temperature continuous method by which liquid waste can be processed with the addition of clay into a sodium aluminosilicate (NAS) waste form. The NAS waste form is mainly comprised of nepheline (NaAlSiO 4 ), sodalite (Na 8 [AlSiO 4 ] 6 Cl 2 ), and nosean (Na 8 [AlSiO 4 ] 6 SO 4 ). Anions such as perrhenate (ReO 4 - ), pertechnetate (TcO 4 - ), and iodine (I - ) are expected to replace sulfate in the nosean structure and/or chloride in the sodalite mineral structure (atomically bonded inside the aluminosilicate cages that these mineral structures possess). In the FBSR waste form, each of these phases can exist in a variety of solid solutions that differ from the idealized forms observed in single crystals in nature. The lack of understanding of the durability of these stoichiometric or idealized mineral phases complicates the ability to deconvolute the durability of the mixed phase FBSR product since it is a combination of different NAS phases. To better understand the behavior, fabrication and testing of the individual phases of the FBSR product is required. Analytical Development (AD) of the Science and Technology directorate of the Savannah River National Laboratory (SRNL) was requested to prepare the series of phase-pure standards, consisting of nepheline, nosean, and Cl, Re, and I sodalite. Once prepared, X-ray Diffraction (XRD) analyses were used to confirm the products were phase pure. These standards are being used for subsequent characterization studies consisting of the following: single-pass flow-through (SPFT) testing, development of thermodynamic data, and x-ray diffraction (XRD) calibration curves. In addition to the above mentioned phase-pure standards, AD was tasked with fabricating a mixed Tc-Re sodalite.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Overview of Los Alamos Support to National Security Missions [Slides]

Los Alamos National Laboratory support for National Security Missions is much broader than what is covered in this introductory briefing. The purpose of this briefing is to provide an introduction to the Laboratory’s support of mission areas that are relevant to the mission of the DNDO and, in particular, to the Operations Support Directorate of DNDO.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗