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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

TaV_BCC_SolidSolution_128atoms_VASP6

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

36 MATERIALS SCIENCE↗
DOE 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 OSTI2026

CHESS 2025: Post-survey report for 2025 NEON AOP Assignable Asset collection of East River and Washington Gulch, Almont and Upper Taylor watersheds at Crested Butte, CO

This report contains details of the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) Research Support Services (RSS) Assignable Asset (AA) flights of the East River, Almont and Upper Taylor watersheds near Crested Butte, CO, June–July 2025. The Rocky Mountain Biological Laboratory (RMBL) contracted the NEON AOP AA flights to observe watersheds of interest near Crested Butte with remotely sensed data including high resolution LiDAR, imaging spectroscopy, and high-resolution camera imagery. The report includes a summary of the acquired flight lines over the planned survey areas, results of calibration flights, and results of the acquired data. The report details how the AOP has met the contracted delivery requirements in terms of the data delivered, quality of the data, and describes issues that resulted in data degradation or data loss. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2019

Fast counting with tensor networks

We introduce tensor network contraction algorithms for counting satisfying assignments of constraint satisfaction problems (#CSPs). We represent each arbitrary #CSP formula as a tensor network, whose full contraction yields the number of satisfying assignments of that formula, and use graph theoretical methods to determine favorable orders of contraction. We employ our heuristics for the solution of #P-hard counting boolean satisfiability (#SAT) problems, namely monotone #1-in-3SAT and #Cubic-Vertex-Cover, and find that they outperform state-of-the-art solvers by a significant margin.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Procuring Stationary Fuel Cells For CHP: A Guide for Federal Facility Decision Makers

Federal agency leaders are expressing growing interest in using innovative fuel cell combined heat and power (CHP) technology at their sites, motivated by both executive branch sustainability targets and a desire to lead by example in the transition to a clean energy economy. Fuel cell CHP can deliver reliable electricity and heat with 70% to 85% efficiency. Implementing this technology can be a high efficiency, clean energy solution for agencies striving to meet ambitious sustainability requirements with limited budgets. Fuel cell CHP systems can use natural gas or renewable fuels, such as biogas. Procuring Stationary Fuel Cells for CHP: A Guide for Federal Facility Decision Makers presents an overview of the process for planning and implementing a fuel cell CHP project in a concise, step-by-step format. This guide is designed to help agency leaders turn their interest in fuel cell technology into successful installations. This guide concentrates on larger (100 kW and greater) fuel cell CHP systems and does not consider other fuel cell applications such as cars, forklifts, backup power supplies or small generators (<100 kW). Because fuel cell technologies are rapidly evolving and have high up front costs, their deployment poses unique challenges. The electrical and thermal output of the CHP system must be integrated with the building s energy systems. Innovative financing mechanisms allow agencies to make a make versus buy decision to maximize savings. This guide outlines methods that federal agencies may use to procure fuel cell CHP systems with little or no capital investment. Each agency and division, however, has its own set of procurement procedures. This guide was written as a starting point, and it defers to the reader s set of rules if differences exist. The fuel cell industry is maturing, and project developers are gaining experience in working with federal agencies. Technology improvements, cost reductions, and experienced project developers are making fuel cell projects easier to put into service. In this environment, federal decision makers can focus on being smart buyers of fuel cell energy instead of attempting to become experts in fuel cell technology. For agencies that want to pursue a fuel cell CHP this guide presents a four step process for a successful project. 1. Perform a preliminary screening of the energy needs energy costs and incentives. 2. Compare a detailed project plan. 3. Make a financing and contracting decision. 4. Execute the project plan including financing, installation, and operation. The simplest procurement method is designated funding for the outright purchase of the fuel cell CHP system, although this is usually not the most cost-effective option. This guide describes the following financing options: Power purchase agreement Energy savings performance contract Utility energy services contract Enhanced use lease Fuel cell CHP technology can help federal facility managers comply with agency objectives for reducing energy consumption and air pollution emissions. Fuel cells do not generate particulate pollutants, unburned hydrocarbons or the gases that produce acid rain. Fuel cells emit less carbon dioxide (CO 2 ) than other, less efficient technologies and use of renewable fuels can make them carbon neutral. Fuel cell CHP technology can deliver reliable electricity and heat with high efficiency (70% to 85%) in a small physical footprint with little noise, making it a cost-effective option for federal facilities.

03 NATURAL GAS↗
DOE OSTI2012

Recovery Act - PEM Fuel Cell Systems Providing Emergency Reserve and Backup Power (Final Technical Report)

The STI product is the Final Technical Report from ReliOn, Inc. for contract award DE-EE0000487: Recovery Act PEM Fuel Cell Systems Providing Emergency Reserve and Backup Power. The program covered the turnkey deployment of 431 ReliOn fuel cell systems at 189 individual sites for AT&T and PG&E with ReliOn functioning as the primary equipment supplier and the project manager. The Final Technical Report provides an executive level summary, a comparison of the actual accomplishments vs. the goals and objectives of the project, as well as a summary of the project activity from the contract award date of August 1, 2009 through the contract expiration date of December 31, 2011. Two photos are included in the body of the report which show hydrogen storage and bulk hydrogen refueling technologies developed as a result of this program.

08 HYDROGEN↗
DOE OSTI2012

Phase II Characterization Survey of the USNS Bridge (T-AOE 10), Military Sealift Fleet Support Command, Naval Station, Norfolk, Virginia (Final Report)

In March 2011, the USNS Bridge was deployed off northeastern Honshu, Japan with the carrier USS Ronald Reagan to assist with relief efforts after the 2011 Tōhoku earthquake and tsunami. During that time, the Bridge was exposed to air-borne radioactive materials leaking from the damaged Fukushima I Nuclear Power Plant. The proximity of the Bridge to the air-borne impacted area resulted in the contamination of the ship’s air-handling systems and the associated components, as well as potential contamination of other ship surfaces due to either direct intake/deposition or inadvertent spread from crew/operational activities. Preliminary surveys in the weeks after the event confirmed low-level contamination within the heating, ventilation, and air conditioning (HVAC) ductwork and systems, and engine and other auxiliary air intake systems. Some partial decontamination was performed at that time. In response to the airborne contamination event, Military Sealift Fleet Support Command (MSFSC) contracted Oak Ridge Associated Universities (ORAU), under provisions of the Oak Ridge Institute for Science and Education (ORISE) contract, to assess the radiological condition of the Bridge. Phase I identified contamination within the CPS filters, ventilation systems, miscellaneous equipment, and other suspect locations that could not accessed at that time (ORAU 2011b). Because the Bridge was underway during the characterization, all the potentially impacted systems/spaces could not be investigated. As a result, MSFSC contracted with ORAU to perform Phase II of the characterization, specifically to survey systems/spaces previously inaccessible. During Phase II of the characterization, the ship was in port to perform routine maintenance operations, allowing access to the previously inaccessible systems/spaces.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2013

Performance Improvement Strategy FY14-18: Create Our Future Strengthen the Core

This document presents the Los Alamos National Laboratory (LANL) Multiyear Strategy for Performance Improvement for FY14–FY18 in accordance with Clause H-3 Contractor Multiyear Strategy for Performance Improvement of the National Nuclear Security Administration (NNSA) Contract No. DE-AC52-06NA25396. “The Contractor shall develop a multiyear strategy that details (1) its planned efforts and expected accomplishments by year, to continuously improve its management and performance at the Laboratory, and (2) the planned efforts and contributions of its Parent Organization. “The multiyear strategy shall also address planned efforts to (1) enhance Contractor communications, cooperation and integration with the NNSA Weapons Complex, with emphasis on Lawrence Livermore National Laboratory; and, (2) contribute to overall NNSA Weapons Complex improvements in performance. The listing of the Contractor’s planned efforts and expected accomplishments from its offer in response to Solicitation No. DE-RP52-05NA25396 for FY2006 (June 1, 2006 – September 30, 2006, partial year) and FY2007 shall be addressed in the initial multiyear strategy. Subsequent annual updates shall be submitted to the Contracting Officer no later than May 15th of each year. Performance measures for these planned efforts and expected accomplishments may be considered for inclusion in the Contract’s Performance Evaluation Plan.”

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Analysis of the Chinese Market for Building Energy Efficiency

China will account for about half of the new construction globally in the coming decade. Its floorspace doubled from 1996 to 2011, and Chinese rural buildings alone have as much floorspace as all of U.S. residential buildings. Building energy consumption has also grown, increasing by over 40% since 1990. To curb building energy demand, the Chinese government has launched a series of policies and programs. Combined, this growth in buildings and renovations, along with the policies to promote green buildings, are creating a large market for energy efficiency products and services. This report assesses the impact of China’s policies on building energy efficiency and on the market for energy efficiency in the future. The first chapter of this report introduces the trends in China, drawing on both historical analysis, and detailed modeling of the drivers behind changes in floorspace and building energy demand such as economic and population growth, urbanization, policy. The analysis describes the trends by region, building type and energy service. The second chapter discusses China’s policies to promote green buildings. China began developing building energy codes in the 1980s. Over time, the central government has increased the stringency of the code requirements and the extent of enforcement. The codes are mandatory in all new buildings and major renovations in China’s cities, and they have been a driving force behind the expansion of China’s markets for insulation, efficient windows, and other green building materials. China also has several other important policies to encourage efficient buildings, including the Three-Star Rating System (somewhat akin to LEED), financial incentives tied to efficiency, appliance standards, a phasing out of incandescent bulbs and promotion of efficient lighting, and several policies to encourage retrofits in existing buildings. In the third chapter, we take “deep dives” into the trends affecting key building components. This chapter examines insulation in walls and roofs; efficient windows and doors; heating, air conditioning and controls; and lighting. These markets have seen significant growth because of the strength of the construction sector but also the specific policies that require and promote efficient building components. At the same time, as requirements have become more stringent, there has been fierce competition, and quality has at time suffered, which in turn has created additional challenges. Next we examine existing buildings in chapter four. China has many Soviet-style, inefficient buildings built before stringent requirements for efficiency were more widely enforced. As a result, there are several specific market opportunities related to retrofits. These fall into two or three categories. First, China now has a code for retrofitting residential buildings in the north. Local governments have targets of the number of buildings they must retrofit each year, and they help finance the changes. The requirements focus on insulation, windows, and heat distribution. Second, the Chinese government recently decided to increase the scale of its retrofits of government and state-owned buildings. It hopes to achieve large scale changes through energy service contracts, which creates an opportunity for energy service companies. Third, there is also a small but growing trend to apply energy service contracts to large commercial and residential buildings. This report assesses the impacts of China’s policies on building energy efficiency. By examining the existing literature and interviewing stakeholders from the public, academic, and private sectors, the report seeks to offer an in-depth insights of the opportunities and barriers for major market segments related to building energy efficiency. The report also discusses trends in building energy use, policies promoting building energy efficiency, and energy performance contracting for public building retrofits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

A Brief Summary on Formalizing Parallel Tensor Distributions, Redistributions, and Algorithm Derivations

Large-scale datasets in computational chemistry typically require distributed-memory parallel methods to perform a special operation known as tensor contraction. Tensors are multidimensional arrays, and a tensor contraction is akin to matrix multiplication with special types of permutations. Creating an efficient algorithm and optimized implementation in this domain is complex, tedious, and error-prone. To address this, we develop a notation to express data distributions so that we can apply use automated methods to find optimized implementations for tensor contractions. We consider the spin-adapted coupled cluster singles and doubles method from computational chemistry and use our methodology to produce an efficient implementation. Experiments per- formed on the IBM Blue Gene/Q and Cray XC30 demonstrate impact both improved performance and reduced memory consumption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The National Carbon Capture Center at the Power Systems Development Facility

The National Carbon Capture Center (NCCC) at the Power Systems Development Facility supports the Department of Energy (DOE) goal of promoting the United States’ energy security through reliable, clean, and affordable energy produced from coal. Work at the NCCC supports the development of new power technologies and the continued operation of conventional power plants under CO 2 emission constraints. The NCCC includes adaptable slipstreams that allow technology development of CO 2 capture concepts using coal-derived syngas and flue gas in industrial settings. Because of the ability to operate under a wide range of flow rates and process conditions, research at the NCCC can effectively evaluate technologies at various levels of maturity and accelerate their development path to commercialization. During its first contract period, from October 1, 2008, through December 30, 2014, the NCCC designed, constructed, and began operation of the Post-Combustion Carbon Capture Center (PC4). Testing of CO 2 capture technologies commenced in 2011, and through the end of the contract period, more than 25,000 hours of testing had been achieved, supporting a variety of technology developers. Technologies tested included advanced solvents, enzymes, membranes, sorbents, and associated systems. The NCCC continued operation of the existing gasification facilities, which have been in operation since 1996, to support the advancement of technologies for next-generation gasification processes and pre-combustion CO 2 capture. The gasification process operated for 13 test runs, supporting over 30,000 hours combined of both gasification and pre-combustion technology developer testing. Throughout the contract period, the NCCC incorporated numerous modifications to the facilities to accommodate technology developers and increase test capabilities. Preparations for further testing were ongoing to continue advancement of the most promising technologies for future power generation processes.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

FutureGen 2.0 Oxy-combustion Large Scale Test – Final Report

The primary objectives of the FutureGen 2.0 CO 2 Oxy-Combustion Large Scale Test Project were to site, permit, design, construct, and commission, an oxy-combustion boiler, gas quality control system, air separation unit, and CO 2 compression and purification unit, together with the necessary supporting and interconnection utilities. The project was to demonstrate at commercial scale (168MWe gross) the capability to cleanly produce electricity through coal combustion at a retrofitted, existing coal-fired power plant; thereby, resulting in near-zeroemissions of all commonly regulated air emissions, as well as 90% CO 2 capture in steady-state operations. The project was to be fully integrated in terms of project management, capacity, capabilities, technical scope, cost, and schedule with the companion FutureGen 2.0 CO 2 Pipeline and Storage Project, a separate but complementary project whose objective was to safely transport, permanently store and monitor the CO 2 captured by the Oxy-combustion Power Plant Project. The FutureGen 2.0 Oxy-Combustion Large Scale Test Project successfully achieved all technical objectives inclusive of front-end-engineering and design, and advanced design required to accurately estimate and contract for the construction, commissioning, and start-up of a commercial-scale "ready to build" power plant using oxy-combustion technology, including full integration with the companion CO 2 Pipeline and Storage project. Ultimately the project did not proceed to construction due to insufficient time to complete necessary EPC contract negotiations and commercial financing prior to expiration of federal co-funding, which triggered a DOE decision to closeout its participation in the project. Through the work that was completed, valuable technical, commercial, and programmatic lessons were learned. This project has significantly advanced the development of near-zero emission technology and will be helpful to plotting the course of, and successfully executing future large demonstration projects. This Final Scientific and Technical Report describes the technology and engineering basis of the project, inclusive of process systems, performance, effluents and emissions, and controls. Further, the project cost estimate, schedule, and permitting requirements are presented, along with a project risk and opportunity assessment. Lessons-learned related to these elements are summarized in this report. Companion reports Oxy-combustion further document the accomplishments and learnings of the project, including: A.01 Project Management Report which describes what was done to coordinate the various participants, and to track their performance with regard to schedule and budget B.02 Lessons Learned - Technology Integration, Value Improvements, and Program Management, which describes the innovations and conclusions that we arrived upon during the development of the project, and makes recommendations for improvement of future projects of a similar nature . B.03 Project Economics, which details the capital and operation costs and their basis, and also illustrates the cost of power produced by the plant with certain sensitivities. B.04 Power Plant, Pipeline, and Injection Site Interfaces, which details the interfaces between the two FutureGen projects B.05 Contractual Mechanisms for Design, Construction, and Operation, which describes the major EPC, and Operations Contracts required to execute the project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Ancillary Service Revenue Potential for Geothermal Generators in California

Achieving California’s 33% renewable generation goal will substantially increase uncertainty and variability in grid operations. Geothermal power plant operators could mitigate this variability and uncertainty by operating plants in a more flexible mode. Plant operators would be compensated for flexibility through payments for ancillary services such as frequency regulation, load following, and spinning reserve. This study explores economic incentives for geothermal plant operators to provide such flexibility. Historical and forecast ancillary service prices are compared to operator compensation for energy under firm contracts at fixed prices, which are higher than current or year 2020 projected market clearing prices for ancillary services in most hours of the year. Power purchase agreements recently executed by geothermal operators typically provide only energy payments at fixed energy prices and escalation rates. We postulate new contract structures that would allow a geothermal plant operator to switch from providing energy to providing ancillary services to the grid operator when it is advantageous to the plant operator to do so. Additional revenues would be earned through ancillary service payments. Estimates of these additional annual revenues a plant operator could realize are developed for a range of contract energy prices. The impacts of flexible operations on reservoir lifetimes and implications for project finance are also discussed.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Ancillary Service Revenue Potential for Geothermal Generators in California FY15 Final Report

Achieving California’s 33% renewable generation goal will substantially increase uncertainty and variability in grid operations. Geothermal power plant operators could mitigate this variability and uncertainty by operating plants in a more flexible mode. Plant operators would be compensated for flexibility through payments for ancillary services such as frequency regulation, load following, and spinning reserve. This study explores economic incentives for geothermal plant operators to provide such flexibility. Historical and forecast ancillary service prices are compared to operator compensation for energy under firm contracts at fixed prices, which are higher than current or year 2020 projected market clearing prices for ancillary services in most hours of the year. Power purchase agreements recently executed by geothermal operators typically provide only energy payments at fixed energy prices and escalation rates. We postulate new contract structures that would allow a geothermal plant operator to switch from providing energy to providing ancillary services to the grid operator when it is advantageous to the plant operator to do so. Additional revenues would be earned through ancillary service payments. Estimates of these additional annual revenues a plant operator could realize are developed for a range of contract energy prices. The impacts of flexible operations on reservoir lifetimes and implications for project finance are also discussed.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Combinatorial Platform for Discovery of Nanocrystal-Ink Based Earth Abundant Element PV with Efficiency Greater than 20% (Final Technical Report)

(1) We successfully developed an ultrasonic spray coating system that can be used to deposit thin chalcogenide films with composition gradients. 4 publications under the contract have been published with the instrument. The instrument was used to reveal the effects of intrinsic composition and examine the effects of 25 different dopant elements. Surprisingly, doping with most elements had little to no effect on the quasi- Fermi level splitting of bare films. A few elements were found to be detrimental including Fe, Co, and Ni. However, Li, Ge, and others were discovered to be beneficial. Ge and Li were explored in depth, and our best devices utilize lithium doping. (2) We developed a new model of absorption coefficients, that when combined with absolute intensity photoluminescence, yield the steady-state quasi-Fermi level splitting and a way to quantify the sub-bandgap absorption. This has resulted in 2 publications on the method, with another in preparation. This is a significant development that should impact other PV technologies. We have already received samples from 2 major PV companies to examine. Further, the model shows that tail states (potential fluctuations) in CZTSSe can only explain about 90 mV of loss in the open circuit voltage. (3) We found that lithium doping has several beneficial effects on CZTSSe. It improves the open-circuit voltage, short circuit current, fill factor, and shunt resistance. By using scanning Kelvin probe microscopy (SKPM) and conductive AFM (along with device measurements, DLPC, and XPS), we discovered that lithium acts to increase the p-type doping in both the grain and grain boundaries (GBs). The effect is stronger in the GBs and changes the direction of the electric field at the GB. In lithium doped devices, an electric field repels minority carrier electrons away from the GB. This resulted in a publication and the fabrication of 11.8% efficient devices from a DMSO-thiourea molecular ink. The mechanism of action is most likely due to the formation of Li Cu , which inhibits the formation of the donor defect Zn Cu . This reduces compensation and increases the net p-type doping. (4) By alloying with germanium, we have fabricated CZTGSSSe devices with the best open-circuit voltage (relative the maximum theoretical open-circuit voltage for the bandgap) for any kesterite solar cell. The Voc/Voc,max is 63%, compared to 58% for the record efficiency cell from hydrazine. The origin of the increased voltage efficiency appears to be related to the conduction band off-set and the suppression of a deep defect ($\sim$0.8 eV), most likely due to Cu Sn , but Sn Zn or Sn Cu are also possible. All milestones and go/no-go metrics were met with exception of the device efficiency milestone (15% then 20%). However, under the contract, hydrazine-free CZTSSe device efficiencies increased from 7.2% at the start of the contract to 11.8% upon completion.

14 SOLAR ENERGY↗
DOE OSTI2017

Combinatorial Platform for Discovery of Nanocrystal-Ink Based Earth Abundant Element PV with Efficiency Greater than 20%

(1) We successfully developed an ultrasonic spray coating system that can be used to deposit thin chalcogenide films with composition gradients. 4 publications under the contract have been published with the instrument. The instrument was used to reveal the effects of intrinsic composition and examine the effects of 25 different dopant elements. Surprisingly, doping with most elements had little to no effect on the quasi-Fermi level splitting of bare films. Ge and Li were explored in depth, and our best devices utilize lithium doping. (2) We developed a new model of absorption coefficients, that when combined with absolute intensity photoluminescence, yield the steady-state quasi-Fermi level splitting and a way to quantify the sub-bandgap absorption. This has resulted in 2 publications on the method, with another in preparation. This is a significant development that should impact other PV technologies. (3) We found that lithium doping has several beneficial effects on CZTSSe. It improves the open-circuit voltage, short circuit current, fill factor, and shunt resistance. By using scanning Kelvin probe microscopy (SKPM) and conductive AFM (along with device measurements, DLPC, and XPS), we discovered that lithium acts to increase the p-type doping in both the grain and grain boundaries (GBs). The effect is stronger in the GBs and changes the direction of the electric field at the GB. In lithium doped devices, an electric field repels minority carrier electrons away from the GB. This resulted in a publication and the fabrication of 11.8% efficient devices from a DMSO-thiourea molecular ink. The mechanism of action is most likely due to the formation of LiCu, which inhibits the formation of the donor defect ZnCu. This reduces compensation and increases the net p-type doping. (4) By alloying with germanium, we have fabricated CZTGSSe devices with the best open-circuit voltage (relative the maximum theoretical open-circuit voltage for the bandgap) for any kesterite solar cell. The Voc/Voc,max is 63%, compared to 58% for the record efficiency cell from hydrazine. The origin of the increased voltage efficiency appears to be related to the conduction band off-set and the suppression of a deep defect (~0.8 eV), most likely due to CuSn, but SnZn or SnCu are also possible. All milestones and go/no-go metrics were met with exception of the device efficiency milestone (15% then 20%). However, under the contract, hydrazine-free CZTSSe device efficiencies increased from 7.2% at the start of the contract to 11.8% upon completion.

14 SOLAR ENERGY↗