Search NASA⌕ Search

SEARCH · Search NASA

Results for “atomic structure”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Relationship between atomic and electronic structure in Ln-bearing oxides

Metastable states of matter are of great interest as they offer the promise of novel functionality. They are often a natural consequence of exposure to nonequilibrium environments. In oxides, metastability can take the form of new polymorphs, chemical disorder, and even amorphization. While significant attention has been given to the impact those changes have on the atomic properties of the material, the corresponding changes in the electronic structure have received less attention. Here, using density functional theory, we consider how the electronic structure varies with potential metastable structures in two classes of lanthanide-bearing oxides—pyrochlores and interlanthanide sesquioxides. We find that the changes depend strongly on both the crystal structure and crystal chemistry of the compound with, for example, disordering and amorphization either increasing or decreasing the bandgap depending on the chemistry. For the 𝐴 2 ⁢𝐵 2 ⁢O 7 pyrochlores, we find different dependencies of the bandgap on the 𝐴 = 𝐿⁢𝑛 cations as the 𝐵 cation is changed, which we relate to the nature of the density of states at the conduction band minimum for different 𝐵 chemistries. Our calculations are validated by electron energy loss spectroscopy measurements for two pyrochlore compounds in which amorphization does reduce the bandgap, consistent with our calculations on these two compounds. In conclusion, our results highlight the relationship between atomic and electronic structure and how radiation can be used to modify and potentially control the electronic properties of oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Carbon doping in GeTe drives differences in local structure and properties

Advances in low-power, energy-efficient information storage and computing require understanding and controlling the atomic and nanoscale structures of functional materials, such as phase-change materials. Phase-change memory technology enables nonvolatile, low-power memory in devices by storing information through reversible changes in a phase-change material's atomic structure (i.e., transformations between amorphous and crystalline phases) that have corresponding changes in properties, including electronic resistivity and optical reflectivity. Here, we apply complementary X-ray absorption spectroscopy and X-ray pair distribution function analyses to experimentally identify the local- and medium-range atomic structure differences of GeTe and C-doped GeTe thin films. Upon controlled heating, composition- and temperature-dependent atomic structure evolution in GeTe and C-doped GeTe films shows differences in bonding behavior and local structure that directly influence crystallization onset temperature. We find that the introduction of C interrupts Ge–Ge bonds in amorphous GeTe, altering the as-deposited structure to be more similar to the distorted rocksalt structure of crystalline α–GeTe. The change alters the response of the amorphous atomic structure to heating and also lowers the crystallization onset temperature, from 230 °C in GeTe to 220 °C in the C-doped film. The combined insights from both X-ray techniques provide understanding of structural transformations that enables the development and optimization of next-generation memory and computing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Metalens formed by structured arrays of atomic emitters

Abstract Arrays of atomic emitters have proven to be a promising platform to manipulate and engineer optical properties, due to their efficient cooperative response to near‐resonant light. Here, we theoretically investigate their use as an efficient metalens. We show that, by spatially tailoring the (subwavelength) lattice constants of three consecutive two‐dimensional arrays of identical atomic emitters, one can realize a large transmission coefficient with arbitrary position‐dependent phase shift, whose robustness against losses is enhanced by the collective response. To characterize the efficiency of this atomic metalens, we perform large‐scale numerical simulations involving a substantial number of atoms (N∼ 5 × 10 5 ) that is considerably larger than comparable works. Our results suggest that low‐loss, robust optical devices with complex functionalities, ranging from metasurfaces to computer‐generated holograms, could be potentially assembled from properly engineered arrays of atomic emitters.

Materials Science↗
DOE OSTI2024

Revealing Structural Evolution of Single Atom Catalysts during Electrochemical CO 2 Reduction by in Situ X-ray Absorption Spectroscopy

Investigating the structural variation of single-atom catalysts (SACs) is crucial to reveal the reaction mechanism under working conditions. Different in situ techniques, especially X-ray absorption spectroscopy (XAS), have been reported to study the structural changes in active sites. However, a systematic study of the relationship between the coordination environment and the catalytic ability of different kinds of SACs is still lacking. Herein, we established isolated transition metal atoms (Fe, Co, Ni, Cu) on N-doped carbon (M-N-C) and employed them in electrocatalytic CO 2 reduction reaction (CO 2 RR). Significantly, Ni-N-C exhibits the highest selectivity (similar to 97.9%) toward CO at -0.8 V vs RHE. In situ XAS characterization discloses the coordination number dependent catalytic performance. Further, the decreased average coordination number of Ni in Ni-N-C at the voltage point with maximum Faradaic efficiency was observed. Density functional theory further provides the possible mechanism of CO 2 -to-CO over the undercoordinated Ni-N-C structure.

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

NbZr_BCC_SolidSolution_128atoms_VASP6

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

36 MATERIALS SCIENCE↗
DOE OSTI2024

TaZr_BCC_SolidSolution_128atoms_VASP6

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

36 MATERIALS SCIENCE↗
DOE OSTI2026

Laser Spectroscopy of Exotic Atoms and Molecules Containing Octupole-Deformed Nuclei

This project investigated the nuclear, atomic, and molecular structure of exotic atoms and molecules containing actinide isotopes. These short-lived radioactive systems are challenging to produce and study in the laboratory, yet they offer unique opportunities for fundamental science. These nuclei are predicted to exhibit pear-shaped (octupole) deformation, a rare collective nuclear behavior that dramatically enhances their sensitivity to fundamental physics phenomena such as time-reversal- and parity-violating effects. Such enhancements make them ideal probes for exploring open questions in our understanding of the universe such as the origin of the matter–antimatter asymmetry of the universe. To realize these measurements, the project led the development of a new laser spectroscopy experiment at MIT, and later commissioned at the Facility for Rare Isotope Beams (FRIB) at Michigan State University: the Resonance Ionization Spectroscopy Experiment (RISE). RISE combines the spectroscopic precision of collinear laser spectroscopy with the sensitivity of particle-detection techniques, enabling measurements of rare isotopes produced at rates as low as a few ions per second. The beamline was designed, built, and installed, and was successfully commissioned at FRIB during the grant period. RISE is now a permanent capability of the FRIB facility, and has produced several results on the study of rare atoms and molecules for nuclear structure and fundamental symmetries. In parallel with the FRIB program, the project contributed to the first precision laser-spectroscopy measurements of short-lived radioactive molecules. Working with international collaborators at CERN's ISOLDE facility, the team conducted pioneering experiments on radium monofluoride (RaF) and actinium monofluoride (AcF). The results from this work have been published in major journals of science, including Nature, Science, Nature Physics, Nature Communications, and Physical Review Letters. These findings have guided future experiments on the laser cooling of radioactive molecules, opening a new platform for precision tests of fundamental symmetries.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Structure refinement and anisotropic atomic displacement parameters of 1M Illite: Rietveld and pair distribution function analysis using synchrotron X-ray radiation

Illite, a widespread clay mineral, plays a pivotal role in geological processes, notably as an indicator in diagenetic and hydrothermal alteration environments, and possesses significant industrial relevance in applications including ceramics, construction and catalysis. However, challenges including its nanoscale crystallinity, structural disorder and frequent interstratification with other clay minerals have hindered detailed structural characterization using conventional X-ray diffraction (XRD) techniques. This study employs integrated synchrotron XRD and pair distribution function (PDF) analysis to elucidate the crystal structure of the 1M illite polytype, yielding the first determination of its anisotropic atomic displacement parameters (U aniso ). TheseU aniso parameters provide critical insights into atomic dynamics and static disorder within the structure, enabling a more refined understanding of structure–property relationships. This integrated approach, combining synchrotron XRD, Rietveld refinement and PDF analysis, yields a comprehensive structural characterization, capturing both average crystallographic and local atomic arrangements. Considering illite's widespread geological occurrence and industrial importance, this high-precision structural dataset, especially the determinedU aniso values, provides a crucial benchmark for future modeling and simulation efforts targeting accurate prediction of its physicochemical behavior.

Chemistry↗
DOE OSTI2025

Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters

Abstract Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure of ice VII with Hirshfeld atom refinement

In the refinement of the crystal structures of ice, the best results obtained so far have been with neutron diffraction because the most troublemaking aspects are the hydrogen atoms. In nine out of twenty ice structures, the hydrogen atoms are disordered, which makes proper refinement more difficult. In our previous paper describing the structure of ice VI we proved that, using Hirshfeld atom refinement (HAR) based on synchrotron X-ray data, it is possible to obtain results comparable with those from neutron experiments. In this work, we investigate another structure of high-pressure disordered ice, cubo-ice (ice VII). Single crystals of cubo-ice were grown under pressure in diamond anvil cells. X-ray diffraction measurements were conducted at a synchrotron source facility (APS, University of Chicago, USA) as well as on our regular in-house laboratory diffractometer with Ag radiation. The data collected were further refined with HAR. Comparison of the structural parameters obtained with those derived from neutron diffraction showed very good agreement in terms of bond lengths and fairly good agreement in terms of hydrogen atom ADPs. We were also able to perform unconstrained refinements with various split-atom models.

Chemistry↗
DOE OSTI2024

Electrochemically Determined and Structurally Justified Thermochemistry of H atom Transfer on Ti-Oxo Nodes of the Colloidal Metal–Organic Framework Ti-MIL-125

Titanium dioxide (TiO 2 ) has long been employed as (photo)electrodes for reactions relevant to energy storage and renewable energy synthesis. Proton-coupled electron transfer (PCET) reactions with equimolar amounts of protons and electrons at the TiO 2 surface or within the bulk structure lie at the center of these reactions. Because a proton and an electron are thermochemically equivalent to an H-atom, these reactions are essentially H-atom transfer reactions. Thermodynamics of H-atom transfer has a complex dependence on the synthetic protocol and chemical history of the electrode, the reaction medium, and many others; together, these complications preclude the understanding of the H-atom transfer thermochemistry with atomic-level structural knowledge. Herein, we report our success in employing open-circuit potential (E OCP ) measurements to quantitatively determine the H-atom transfer thermochemistry at structurally well-defined Ti-oxo clusters within a colloidally stabilized metal–organic framework (MOFs), Ti-MIL-125. The free energy to transfer H-atom, Ti 3+ O–H bond dissociation free energy (BDFE), was measured to be 68(2) kcal mol -1 . To the best of our understanding, this is the first report on using E OCP measurements to quantify thermochemistry on any MOFs. The proton topology, the structural change upon the redox reaction, and BDFE values were further quantitatively corroborated using computational simulations. Furthermore, comparisons of the E OCP -derived BDFEs of Ti-MIL-125 to similar parameters in the literature suggest that E OCP should be the preferred method for quantitatively accurate BDFE calculations. Here, the reported success in employing E OCP for nanosized Ti- MIL-125 should lay the ground for thermochemical measurements of other colloidal systems, which are otherwise challenging. Implications of these measurements on Ti-MIL-125 as an H-atom acceptor in chemical reactions and comparisons with other MOFs/metal oxides are discussed.

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

Structural Complexities in Sodium Ion Conductive Antiperovskite Revealed by Cryogenic Transmission Electron Microscopy

Here we use low-dose cryogenic transmission electron microscopy (cryo-TEM) to investigate the atomic-scale structure of antiperovskite Na 2 NH 2 BH 4 crystals by preserving the room-temperature cubic phase and carefully monitoring the electron dose. Via quantitative analysis of electron beam damage using selected area electron diffraction, we find cryogenic imaging provides 6-fold improvement in beam stability for this solid electrolyte. Cryo-TEM images obtained from flat crystals revealed the presence of a new, long-range-ordered supercell with a cubic phase. The supercell exhibits doubled unit cell dimensions of 9.4 Å × 9.4 Å as compared to the cubic lattice structure revealed by X-ray crystallography of 4.7 Å × 4.7 Å. The comparison between the experimental image and simulated potential map indicates the origin of the supercell is a vacancy ordering of sodium atoms. This work demonstrates the potential of using cryo-TEM imaging to study the atomic-scale structure of air- and electron-beam-sensitive antiperovskite-type solid electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Time-resolved atomic-resolution Brownian tomography of single nanocrystals reveals size-dependent dynamics

Atomic-resolution structure identification of nanocrystals by graphene liquid cell electron microscopy (GLC-EM) has revealed that small, solubilized platinum nanocrystals consist of an ordered crystalline core surrounded by mobile surface atoms, which dissociate during oxidative etching, resulting in distinct temporal structural states. Requirements imposed by the 3D reconstruction algorithm limit the number of structural states that can be resolved. We introduce a regularized 3D reconstruction algorithm that exploits the redundancy inherent in the experimental data, allowing us to improve the time resolution. Our developments provide a comprehensive molecular picture at unprecedented spatial and temporal resolution of the nonlinear, linear, and fluctuating dynamic phenomena that single nanocrystals undergo during the GLC-EM experiment. We determined atomic structures of 66 temporal structural states, extracted from 15 time trajectories of individual nanocrystals. Large (478 to 698 atoms) and small (<300 atoms) nanocrystals show etching that preserves a stable core, whereas mid-sized (351 to 571 atoms) nanocrystals present dynamics that change the coordination of the core.

Meana-Pañeda, Rubén↗
DOE OSTI2025

Kinetics of short-range order formation in GeSn alloy: MBE vs CVD

Recently, short-range order (SRO) has attracted significant attention, challenging the conventional view of the atomic positions in alloys being random. Furthermore, the presence of SRO has been predicted to have profound effects on the electronic and topological properties of group-IV alloys, offering a different direction in designing group-IV materials for photoelectronic and quantum devices. However, due to the limited understanding of the formation mechanisms, developing effective methods to manipulate SRO in epitaxy is still challenging. To address this, we propose a mechanism for the GeSn alloy, revealing that surface diffusion plays a key role in SRO formation. Building on this mechanism, we show that the distinct surface conditions in MBE and CVD lead to the formation of SRO with enhanced Sn–Sn pairing in MBE-grown samples, while CVD-grown samples remain random alloys. Furthermore, our findings provide an initial understanding of the kinetic process of SRO formation, providing guidance for the design of experiments to manipulate SRO.

Alloys↗
DOE OSTI2026

A chemical bonding based descriptor for predicting the role of anharmonicity induced by quantum nuclear effects in hydride superconductors

Quantum nuclear effects (QNEs) can significantly alter a material’s crystal structure and phonon spectra, impacting properties such as thermal conductivity and superconductivity. However, predicting a priori whether these effects will enhance or suppress superconductivity, or destabilize a structure, remains a grand challenge. Herein, we address this unresolved problem by introducing two possible descriptors, based upon the integrated crystal orbital bonding index (iCOBI) or the bond valence function, to predict the influence of QNEs on a crystal lattice’s dynamic stability, phonon spectra and superconducting properties. We find that structures with atoms in symmetric chemical bonding environments exhibit greater resilience to structural perturbations induced by QNEs, while those with atoms in asymmetric bonding environments are more susceptible to structural alterations, resulting in enhanced superconducting critical temperatures.

Physical chemistry↗
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2024

Phonon Screening of Excitons in Atomically Thin Semiconductors

Atomically thin semiconductors, encompassing both 2D materials and quantum wells, exhibit a pronounced enhancement of excitonic effects due to geometric confinement. Consequently, these materials have become foundational platforms for the exploration and utilization of excitons. Recent ab initio studies have demonstrated that phonons can substantially screen electron-hole interactions in bulk semiconductors and strongly modify the properties of excitons. While excitonic properties of atomically thin semiconductors have been the subject of extensive theoretical investigations, the role of phonon screening on excitons in atomically thin structures remains unexplored. In this Letter, we demonstrate via ab initio GW-Bethe-Salpeter equation calculations that phonon screening can have a significant impact on optical excitations in atomically thin semiconductors. We further show that the degree of phonon screening can be tuned by structural engineering. We focus on atomically thin GaN quantum wells embedded in AlN and identify specific phonons in the surrounding material, AlN, that dramatically alter the lowest-lying exciton in monolayer GaN via screening. Our studies provide new intuition beyond standard models into the interplay among structural properties, phonon characteristics, and exciton properties in atomically thin semiconductors, and have implications for future experiments.

2-dimensional systems↗
DOE OSTI2025

Strain-driven oxygen vacancy ordering in LaNiO 3 thin films revealed by integrated differential phase contrast imaging in scanning transmission electron microscopy

Rare-earth nickelates, such as LaNiO 3 (LNO), exhibit complex electronic properties, with ordered oxygen vacancies (OOV) influencing conductivity and magnetic behavior. We investigate the structural stability of strain-induced OOV phases in LNO thin films grown on SrTiO 3 substrates and the impact of Ruddlesden–Popper (RP) faults. Using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and integrated differential phase contrast (iDPC) STEM imaging, we conducted atomic-scale structural and compositional analyses of OOV. Geometric phase analysis (GPA) was employed to measure the strain in fault-free and RP fault regions, while density functional theory (DFT) calculations explored different OOV arrangements in the LNO phase. Simulated iDPC-STEM imaging of energy-stabilized structures was performed to correlate with experimental results. Here, our findings reveal superstructure modulation in the chemical composition and atomic-scale lattice structure in LNO, primarily due to the formation of the OOV in Ni–O layers of the LaNiO 2.5 phase. The out-of-plane compressive strain of about 2% stabilizes this phase, reducing the strain, diminishing OOV, and transforming them into LNO.

36 MATERIALS SCIENCE↗