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At least 37 records · Page 2

690-P_400C_1DPA_(ID-408)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

690-P_400C_3DPA_(ID-621)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

G91-C_400C_1DPA_(ID-425)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

G91-C_400C_3DPA_(ID-666)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

G91-P_400C_1DPA_(ID-424)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

G91-P_400C_3DPA_(ID-636)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

SA508-F_300C_1DPA_(ID-110)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

SA508-F_300C_1DPA_(ID-114)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

SA508-F_400C_1DPA_(ID-437)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

SA508-P_300C_1DPA_(ID-104)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

SA508-P_400C_1DPA_(ID-434)

This is the mechanical testing data archive from a neutron irradiation campaign of nuclear structural alloys fabricated by powder metallurgy with hot isostatic pressing (PM-HIP). Data in this archive include uniaxial tensile testing data, fractography of selected tensile bars, and nanoindentation. The irradiation campaign was designed to facilitate a direct comparison of PM-HIP to conventional casting or forging. Five common nuclear structural alloys were included in the campaign: 316L stainless steel, SA508 pressure vessel steel, Grade 91 ferritic steel, and Ni-base alloys 625 and 690. Irradiations were carried out in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to target doses of 1 and 3 displacements per atom (dpa) at target temperatures of 300ºC and 400ºC through the DOE Office of Nuclear Energy, Nuclear Science User Facilities (NSUF) project 15-8242.

Wharry, Janelle↗

Summer 2025 SULI: Nucleus ID, TinyTPC, and Scientific Communication

This paper summarizes my work during the Summer 2025 SULI internship, which focused on two main projects and broader scientific development. The first project involved improving the particle identification (PID) of protons, deuterons, and tritons using PIDA distributions and template fitting, with the goal of modeling nuclear final-state interactions (FSI) and testing the robustness of the method against systematic uncertainties. These techniques pave the way for future application to LArTPC data from the ICARUS detector. The second project centered on the optimization and data-taking of the TinyTPC detector, a compact LArTPC used for high-resolution low-energy measurements. I adjusted gain and threshold parameters, performed hardware validation tests, and developed analysis strategies to extract meaningful physics from collected data. Throughout the summer, I also enhanced my scientific communication and mentorship skills through presentations, collaborative analysis, and peer guidance.

McCright, Hannah [Maryland U.]↗

NbZr_BCC_SolidSolution_128atoms_VASP6

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

36 MATERIALS SCIENCE↗

TaZr_BCC_SolidSolution_128atoms_VASP6

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

36 MATERIALS SCIENCE↗

DOE EV Data Collection - Charging Data

Charging data are collected from one of three sources, each with varying levels of additional information. These sources, in approximate order from most to least additional information, are: • The electric vehicle supply equipment (charger) • Onboard the vehicle itself • From a utility submeter. Many chargers provide software that allows for the collection and reporting of charging session data. If unavailable, data may be recorded by the charging vehicle’s onboard systems. If neither of these options is available, data can be acquired from utility submeters that simply track the energy flowing to one or more chargers. Data collected directly from the electric vehicle supply equipment (EVSE) are typically the most accurate and highest frequency. However, it is not always possible to discern which exact vehicle is being charged during any one session. EVSE-side data can be identified where a single charger ID but a range of vehicle IDs are present (e.g., CH001, EV001-EV005). Data collected from the vehicle’s onboard systems usually does not provide information on which exact charger is being used. Vehicle-side data can be identified where a single Vehicle ID but a range of Charger IDs are present (e.g., EV001, CH001-CH005). Data collected from utility submeters provide no information on which specific vehicle is charging or which specific charger is in use. Submeter data can be identified where multiple Vehicle IDs and multiple Charger IDs are present, but only a single Fleet ID is present (e.g., EV001-EV005, CH001-CH005, Fleet01). The **Charge Data Daily/Session Dictionaries** contains definitions for each available parameter collected as part of an individual charging session, aggregated at either a daily or session level. The parameters available will vary between vehicles and chargers. The **Charger Attributes** table contains specific charger characteristics, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. The **Charger Attributes Data Dictionary** contains definitions for each available parameter collected on the physical and operational characteristics of the charging hardware itself. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables, and in cases where charging data are supplied, links a vehicle with the charger(s) that supplied it power. The **Charging Data** tables contain the data from each charger’s operations, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗

Correction: “When I talk about it, my eyes light up!” Impacts of a national laboratory internship on community college student success

The ORCID iDs are missing for the second, fourth, sixth, seventh, eighth and ninth author. Please see the authors’ respective ORCID iDs here: Author Seth Van Doren’s ORCID iD is: 0000-0003-0674-277X (https://orcid.org/0000-0003-0674-277X). Author Julio Jaramillo Salcido’s ORCID iD is: 0000-0002-4113-5345 (https://orcid.org/orcid.org/0000-0002-4113-5345) Author Gabriel Otero Munoz’s ORCID iD is: 0000-0002-1444-8020 (https://orcid.org/0000-0002-1444-8020) Author Aparna Manocha’s ORCID iD is: 0000-0001-7824-9971 (https://orcid.org/0000-0001-7824-9971) Author Colette L. Flood’s ORCID iD is: 0000-0002-8674-0872 (https://orcid.org/0000-0002-8674-0872) Author Anne M. Baranger’s ORCID iD is: 0000-0002-1973-4632 (https://orcid.org/0000-0002-1973-4632)

99 GENERAL AND MISCELLANEOUS↗

Creep Property of Intermetallic Dispersive Steels for Nuclear Applications

To meet the materials performance demands of next-generation nuclear and high-temperature energy systems, a new class of intermetallic-dispersive steels (IDS) has been designed through integrated computational thermodynamics and alloy design strategies. The IDS alloys incorporate coherent L1₂ (γ′-Ni₃Al) nanoprecipitates within an Fe–Ni–Cr austenitic matrix, engineered to achieve both high temperature strength and thermodynamic stability while suppressing the formation of detrimental δ-Ni₃Nb and η-Ni₃Ti phases. Initial creep testing of Ti-IDS and TiTa-IDS alloys demonstrates rupture lives comparable to or exceeding those of Inconel 718 and ODS steels, despite being fabricated through conventional ingot metallurgy. Step-load creep tests identified a stress threshold near 300–350 MPa for the onset of tertiary creep, and in-situ neutron diffraction experiments on Ti-IDS revealed clear load partitioning between the matrix and γ′ precipitates: elastic strain is shared by both phases, while plastic strain localizes in the matrix. These results confirm that stable γ′–matrix interfaces play a dominant role in retarding dislocation motion and enhancing creep resistance. In FY26, the program will conduct repeat creep rupture testing of TiTa-IDS at 650 °C/400 MPa, initiate long-term rupture testing of TiNb-IDS, and perform TEM-based microstructural characterization to elucidate dislocation–precipitate interactions and microstructural stability. Collectively, these efforts will establish the mechanistic foundation for next-generation high-temperature structural materials based on the IDS concept.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗