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DOE OSTI2026

Theory Guided Fine‐Tune of Strain Effects in Pt Ternary Alloy via Rare Earth Templating: Achieving High Performance PEMFCs Catalysts

The sluggish kinetics and insufficient durability of platinum-based catalysts remain crucial barriers limiting proton-exchange-membrane fuel cells (PEMFCs) deployment. Here, we report a theory-guided synthesis combined with rare-earth templating to realize a previously inaccessible Pt 5 Co-like phase with tailored atomic-scale strain. Guided by density functional theory (DFT) calculations, we identified that a Pt 5 Co-like sublayer can induce a unique mild compressive strain (−1.24%) to the Pt(111) shell and an optimal *OH binding energy shift (ΔE ≈ 0.11 eV). This shift positions the alloy catalyst near the apex of the oxygen reduction reaction activity volcano. This prediction guided the synthesis of ternary alloy Pt 5 (Ce)Co@Pt multilayer nanoparticles, featuring a Ce-stabilized core, a Pt 5 Co-like sublayer, and a Pt-rich shell. This catalyst demonstrates both exceptionally high activity and durability, achieving a mass activity of 2.6 A∙mg Pt −1 in rotating disk electrode testing. In fuel cell membrane electrode assembly tests, Pt 5 (Ce)Co@Pt achieves a current density of 1.9 A∙cm −2 at 0.7 V under heavy-duty vehicle conditions. Remarkably, it maintains 1.2 A∙cm −2 after 1 80 000 AST cycles, doubling the U.S. DOE 2025 target. This work demonstrates a rational design strategy that DFT-guided strain engineering integrates with rare-earth templating to advance Pt-based catalysts for fuel cell applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Density Functional Theory Guided Investigation of Ligand‐Induced Neptunyl‐Neptunyl Interactions

Abstract Actinyl‐actinyl interactions are particularly prevalent for the pentavalent neptunyl cation (Np(V)O 2 ) + where these interactions appear either as a T ‐ or D ‐shape (diamond‐shape). T ‐shaped interactions have been previously identified in high concentration Np(V) solutions containing simple anions (NO 3 − , ClO 4 − , Cl − ) whereas D ‐shaped have only been isolated in the solid‐state in the presence of carboxylate ligands. In this study, Density Functional Theory (DFT) calculations were paired with Raman spectroscopy to evaluate the formation of D ‐shaped interactions in the presence of aliphatic (R=H (formate), CH 3 (acetate), CH 2 CH 3 (propionate)) and aromatic (R=C 6 H 5 (benzoate), C 6 H 4 OH (4‐hydroxybenzoate), C 5 H 4 N (isonicotinate)) carboxylate ligands. DFT studies indicate that the Δ G to form hydrated T ‐ and D ‐shaped forms are not spontaneous but become so with the addition of the carboxylate ligands. Raman spectra of the Np(V) carboxylate solutions contained vibrational modes associated with the D ‐shaped interactions, but spectral changes observed over time indicate a dynamic system. Crystallization experiments from the Np(V) carboxylate systems confirmed the presence of D‐ shaped dimers for the aromatic carboxylates, suggesting that the choice of the anion in solution favors actinyl‐actinyl interactions even at low concentrations (≤20 mM) of Np(V).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2022

Magnetization reversal driven by electron localization-delocalization crossover in the inverse spinel Co 2 VO 4

Neutron diffraction, magnetization, and muon spin relaxation measurements, supplemented by density functional theory (DFT) calculations are employed to unravel temperature-driven magnetization reversal in inverse spinel Co 2 VO 4 . All measurements show a second-order magnetic phase transition at T C = 168 K to a collinear ferrimagnetic phase. Neutron diffraction measurements reveal two antiparallel ferromagnetic (FM) sublattices, belonging to magnetic ions on two distinct crystal lattice sites, where the relative balance between the two sublattices determine the net FM moment in the unit cell. As the evolution of the ordered moment with temperature differs between the two sublattices, the net magnetic moment reaches a maximum at T NC = 138 K and reverses its sign at T MR = 65 K. The DFT results suggest that the underlying microscopic mechanism for the reversal is a delocalization of the unfilled 3d -shell electrons on one sublattice just below T C , followed by a gradual localization as the temperature is lowered. Furthermore, this delocalized-localized crossover is supported by muon spectroscopy results, as strong T 1 relaxation observed below T C indicates fluctuating internal fields.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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 OSTI2022

Structural and Bonding Analysis in Monomeric Actinide(IV) Oxalate from Th(IV) to Pu(IV): Comparison with the An(IV) Nitrate Series

In this work, single-crystal X-ray diffraction (SC-XRD) structures and Raman spectra of a series of new isomorphous molecular An(IV)-oxalate compounds (Th, U, Np, and Pu) are reported. These complexes are crystallized with cobalt(III) hexamine ([Co(NH 3 ) 6 ] 3+ ) as the counter cations, [Co(NH 3 ) 6 ] 2 [An(C 2 O 4 ) 5 ]·4H 2 O, revealing five bidentate nonbridging oxalate ligands in the first coordination sphere (CN = 10). The nonbridging oxalate is rather uncommon for An(IV)-oxalate systems, which are widely characterized as polymeric compounds. Density functional theory (DFT) calculations were performed to examine the bonding between An(IV) cations and oxalate ligands. For comparison, we also report results obtained for the An(IV)-hexanitrate series, [(C 2 H 5 ) 4 N] 2 [An(NO 3 ) 6 ] (with An = Th, U, Np, Pu, and Ce), which consists of O-donor ligands as well but with a larger coordination number (CN = 12). The bonding analysis confirms that the actinide–oxygen bond is predominantly ionic with a minor increase in covalency from Th to U and slight variations from U to Pu. Further comparison showed that the charge transfer increases slightly when increasing the number of anions in the coordination sphere (C 2 O$_4^{2–}$: CN = 10; NO$_3^–$: CN = 12), but covalent effects as indicated by the amount of internuclear electron density accumulation are small and similar for oxalate and nitrate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Oxygen Deficiency and Migration-Mediated Electric Polarization in Magnetic Fe,Co-Substituted SrTiO3−δ

We use density functional theory (DFT) calculations to show that oxygen vacancies (vO) and mobility induce noncentrosymmetric polar structures in SrTi1−x−yFexCoyO3−δ (STFC, x=y=0.125) with δ={0.125,0.25}, enhance the saturation magnetization, and give rise to large changes in the electric polarization |ΔP|. We present an intuitive set of rules to describe the properties of STFC, which are based on the interplay between (Co/Fe)-vO defects, magnetic cation coordination, and topological vacancy disorder. STFC structures consist of layered crystals with sheets of linearly organized O4,5,6-coordinated Fe–Co pairs, sandwiched with layers of O5-coordinated Ti. (Co/Fe)-vO defects are the source of crystal distortions, cation off-centering and bending of the oxygen octahedra which, considering the charge redistribution mediated by vO and the cations’ electronegativity and valence states, triggers an effective electric polarization. Oxygen migration for δ=0.125 leads to |ΔP|>∼10 µC/cm2 due to quantum-of-polarization differences between δ=0.125 structures. Increasing the oxygen deficiency to δ=0.25 yields |ΔP|, the O migration of which resolved polarization for δ=0.25 is >∼3 µC/cm2. Magnetism is dominated by the Fe,Co spin states for δ=0.125, and there is a contribution from Ti magnetic moments (∼1 μB) for δ=0.25. Magnetic and electric order parameters change for variations of δ or oxygen migration for a given oxygen deficiency. Our results capture characteristics observed in the end members of the series SrTi(Co,Fe)O3, and suggest the existence of a broader set of rules for oxygen-deficient multiferroic oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2023

TaV_BCC_SolidSolution_128atoms_VASP6

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

36 MATERIALS SCIENCE↗
DOE OSTI2023

NbTa_BCC_SolidSolution_128atoms_VASP6

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

36 MATERIALS SCIENCE↗
DOE OSTI2025

Site-specific surface reactivity on SnO 2 : Evaluating selective atomic layer deposition processes

Area selective atomic layer deposition (AS-ALD) is a bottom-up synthesis approach with potential for deposition with molecular level precision. Here, the site-specific hydration of metal oxide substrates, combined with surface H 2 O-selective ALD processes, provides a potentially powerful path to targeted synthesis. Density functional theory (DFT) calculations are used to predict the thermodynamics of ALD precursor reactivity and hydration for (001), (101), (110), and (100) rutile SnO 2 facets as a function of temperature. Trimethylaluminum (TMA) and dimethyl aluminum isopropoxide (DMAI) dimers are predicted to react with both dehydrated and hydrated SnO 2 (001), (101), and (110) facets at ALD-relevant temperatures, while the SnO 2 (100) facet is predicted to be uniquely unreactive with TMA and DMAI monomers as well as dehydrate near 177 °C making this facet more amenable to targeted ALD. In situ ellipsometric studies of Al 2 O 3 ALD on polycrystalline SnO 2 at 150 °C are consistent with the computational predictions of rapid and unselective nucleation, in stark contrast to inhibited and selective ALD on isostructural rutile TiO 2 .

Atomic Layer Deposition↗
DOE OSTI2025

HfNb_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 hafnium-niobium (Hf-Nb). 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 elements Hf and Nb. 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 3,029 randomized atomic structures over 31 chemical compositions. Further methodological and structural information is contained in the dataset README.txt file.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Gas-phase negative ion photoelectron spectroscopy and reactivity of phenylacetylide

Arylacetylides such as phenylacetylide (PhCC–, 1) are important nucleophiles used in synthetic chemistry yet rarely have they been studied as bare carbanions. In this work, the phenylacetylide anion was formed via electrospray ionization (ESI) or multistage mass spectrometry (MSn) experiments and subsequently examined in the gas phase by negative ion photoelectron spectroscopy (NIPES), ion-molecule reactions (IMR), alongside theoretical calculations to probe its fundamental structure and reactivity. Photoelectron spectra of PhCC– (1) revealed vertical (VDE) and adiabatic detachment energies (ADE), both of 3.220 eV. The latter value is also the electron affinity (EA) of the phenylethynyl radical (PhCC•) from which a bond dissociation energy (BDE) of phenylacetylene (PhCCH) was derived to be ca. 131.3 ± 1.7 kcal mol-1 using a gas-phase thermochemical cycle. Detachment of an electron from PhCC– at 266 nm likely results in resonant autodetachment, supported by Franck-Condon Factor (FCF) simulations. The phenylacetylide anion reacts with methyl iodide (CH3I) and allyl iodide (C3H5I) via SN2 nucleophilic displacement with measured rate coefficients of 4.29 and 3.66 × 10-10 cm3 molecule-1 s-1, respectively. The mechanisms associated with these displacement reactions are explained and understood in terms of the reaction kinetics, natural bond orbital (NBO) theory, and Density Functional Theory (DFT) calculations.

Ma, Howard Z.↗
DOE OSTI2021

Understanding the origin of structure sensitivity in hydrodechlorination of trichloroethylene on a palladium catalyst

Periodic density functional theory (DFT) calculations are employed to understand the origin of structure sensitivity in hydrodechlorination (HDC) of trichloroethylene (TCE) over different facets of a palladium catalyst. The HDC reaction is simulated on the terrace (Pd (111) and Pd (100)) and undercoordinated (Pd (211) and Pd (110)) sites of the Pd catalyst. The most stable binding configuration of TCE on the Pd surfaces is observed to be through the di-σ mode of binding, wherein each carbon atom of the TCE molecule is adsorbed atop of the Pd atom. On comparing TCE adsorption over different facets of Pd, a maximum binding energy of –178 kJ mol –1 is calculated over the Pd (110) surface. TCE, upon adsorption on Pd catalyst, undergoes dechlorination followed by hydrogenation of the hydrocarbon intermediates. The activation energies for C–Cl bond dissociation steps are significantly low when compared to the hydrogenation steps. The chlorine released from dechlorination tends to block the active sites, thereby poisoning the surface with high binding energies (B.E > –160 kJ mol –1 ) on all the surfaces. The trend in chlorine binding energies on Pd facets follows: Pd (110) > Pd (211) > Pd (100) > Pd (111). The removal of surface chlorine is facilitated by its reaction with surface hydrogen to form hydrogen chloride. The activation energy for hydrogen chloride formation is calculated to be 90 kJ mol –1 and 88 kJ mol –1 on Pd (111) and Pd (100) terrace sites, respectively and 109 kJ mol –1 and 118 kJ mol –1 on the step Pd (211) and corrugated Pd (110) facets, respectively. This suggests the ease of removal of Cl as HCl from the terrace sites as compared to the step and corrugated sites. The structure sensitivity in the TCE HDC reaction could possibly arise due to the differences in the energetics of Cl removal on different Pd facets. As a result, this mechanistic understanding could provide a rationale for designing suitable catalysts for the HDC of TCE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Robustness of magnetocrystalline anisotropy and coercivity in Fe–Co–B

As a potential candidate for critical chemical element free permanent magnet, (Fe 0.7 Co 0.3 ) 2 B, shows promising intrinsic magnetic properties, including large magnetization, high Curie temperature and moderate magnetocrystalline anisotropy (MCA). Still, the coercivity that has been achieved is relatively low. To understand and improve coercivity, here we study the robustness of MCA related to the fluctuation of chemical composition and the structural distortion and its effect on coercivity in (Fe 0.7 Co 0.3 ) 2 B from first principles DFT (density functional theory) calculation and micromagnetic simulation. A fluctuation of cobalt content of 10%, or 1% isotropic lattice strain, or 2% tetragonal distortion reduces MCA energy up to 30%. By considering randomly distributed local MCA energy reduction up to 30%, the calculated coercivity of anisotropic bulk magnets reduced from 3.6 kOe to 2.2 kOe. Rational microstructure design, such as grain size refinement and development of strong crystal texture of MCA easy-axis (i.e., degree of alignment for (Fe 0.7 Co 0.3 ) 2 B grains), can enhance coercivity and mitigate the effect of local MCA reduction. In addition to acceptable intrinsic magnetic properties, the promising hard magnetic phase needs good MCA robustness to facilitate the development of practical magnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Exploring mechanistic routes for light alkane oxidation with an iron–triazolate metal–organic framework

In this work, we computationally explore the formation and subsequent reactivity of various iron-oxo species in the iron–triazolate framework Fe 2 (m-OH) 2 (bbta) (H2bbta = 1H,5H-benzo(1,2-d:4,5- d0)bistriazole) for the catalytic activation of strong C–H bonds. With the direct conversion of methane to methanol as the probe reaction of interest, we use density functional theory (DFT) calculations to evaluate multiple mechanistic pathways in the presence of either N 2 O or H 2 O 2 oxidants. These calculations reveal that a wide range of transition metal-oxo sites – both terminal and bridging – are plausible in this family of metal–organic frameworks, making it a unique platform for comparing the electronic structure and reactivity of different proposed active site motifs. Based on the DFT calculations, we predict that Fe 2 (m-OH)2(bbta) would exhibit a relatively low barrier for N 2 O activation and energetically favorable formation of an [Fe(O)] 2+ species that is capable of oxidizing C–H bonds. In contrast, the use of H 2 O 2 as the oxidant is predicted to yield an assortment of bridging iron-oxo sites that are less reactive. We also find that abstracting oxo ligands can exhibit a complex mixture of both positive and negative spin density, which may have broader implications for relating the degree of radical character to catalytic activity. In general, we consider the coordinatively unsaturated iron sites to be promising for oxidation catalysis, and we provide several recommendations on how to further tune the catalytic properties of this family of metal–triazolate frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational investigations of Dienes defect- and vacancy-induced changes in the electronic and vibrational properties of carbon fiber structural units

Carbon fiber (CF) is a promising lightweight alternative to steel and is of significant interest for energy applications. As CF continues to find new uses and is exposed to new external conditions, a noninvasive method of monitoring its structural integrity is critical. Raman spectroscopy is a commonly used method for this monitoring; however, it is highly inferential, and the interpretation of the data is not always straightforward. In this work, we perform density functional theory (DFT) calculations to investigate changes in the vibrational properties of CF structural units (i.e., graphene and graphite) caused by monovacancy and Dienes defects as a foundation for modeling more complex defects that move our model toward that of realistic CF. Using large computational supercells, we can understand how these defects change the electronic structure and vibrational properties of graphene and graphite for interdefect distances near those of the lower experimental limit. The monovacancy opens an electronic bandgap at the K point. Although no such electronic gap is opened by the Dienes defect, both defects introduce flat defect bands near the Fermi energy. The Dienes defect creates long-range deviations of the phonons, leading to substantial broadening of the highest frequency optical modes in the band structure compared to that of the pristine material. In contrast, the phonon changes caused by the monovacancy are short range, and only minor changes in the band structure or phonon density of states were observed. These findings can assist in the interpretation of experimental results by providing atomic-scale insight into key electronic and vibrational features.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Regulating Catalytic Properties and Thermal Stability of Pt and PtCo Intermetallic Fuel-Cell Catalysts via Strong Coupling Effects between Single-Metal Site-Rich Carbon and Pt

Developing low platinum-group-metal (PGM) catalysts for the oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs) for heavy-duty vehicles (HDVs) remains a great challenge due to the highly demanded power density and long-term durability. Here, this work explores the possible synergistic effect between single Mn site-rich carbon (Mn SA -NC) and Pt nanoparticles, aiming to improve intrinsic activity and stability of PGM catalysts. Density functional theory (DFT) calculations predicted a strong coupling effect between Pt and MnN 4 sites in the carbon support, strengthening their interactions to immobilize Pt nanoparticles during the ORR. The adjacent MnN 4 sites weaken oxygen adsorption at Pt to enhance intrinsic activity. Well-dispersed Pt (2.1 nm) and ordered L1 2 -Pt 3 Co nanoparticles (3.3 nm) were retained on the Mn SA -NC support after indispensable high-temperature annealing up to 800 °C, suggesting enhanced thermal stability. Both PGM catalysts were thoroughly studied in membrane electrode assemblies (MEAs), showing compelling performance and durability. The Pt@Mn SA -NC catalyst achieved a mass activity (MA) of 0.63 A mg Pt –1 at 0.9 V iR-free and maintained 78% of its initial performance after a 30,000-cycle accelerated stress test (AST). The L1 2 -Pt 3 Co@Mn SA -NC catalyst accomplished a much higher MA of 0.91 A mg Pt –1 and a current density of 1.63 A cm –2 at 0.7 V under traditional light-duty vehicle (LDV) H 2 –air conditions (150 kPa abs and 0.10 mg Pt cm –2 ). Furthermore, the same catalyst in an HDV MEA (250 kPa abs and 0.20 mg Pt cm –2 ) delivered 1.75 A cm –2 at 0.7 V, only losing 18% performance after 90,000 cycles of the AST, demonstrating great potential to meet the DOE targets.

25 ENERGY STORAGE↗