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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Combining Reactive Quantum-Mechanical Molecular-Dynamics Simulations with Mutagenesis, Crystallography, and Enzyme Kinetics to Reveal Plausible Steps of Isocyanide Hydratase Catalysis

A complete understanding of enzyme mechanisms requires atomistic details of chemical reactions. Quantum-based molecular dynamics simulations (QMD) are a potential source of this information, but trade-offs between accuracy and computational cost have limited their use. We previously developed extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) methods that leverage a negligible compromise in accuracy to substantially decrease the cost of QMD simulations. Here, we develop a reactive QMD approach using the latest XL-BOMD formulation, which enables efficient simulations of highly reactive systems, and use it to investigate mechanisms of intermediate formation in isocyanide hydratase (ICH) catalysis. In QMD simulations, molecular analogs of ICH active site residues reacted with para-nitrophenyl isocyanide, forming a thioimidate. Analysis of simulated atomic configurational and charge dynamics revealed a pathway where protonation of the isocyanide carbon occurs prior to thioimidate formation and suggested a possible role of Asp17 as a proton donor in the early phase of ICH catalysis. To test whether the pathway seen using the reactive QMD approach might be relevant to ICH catalysis, we performed X-ray crystallography and pre-steady-state enzyme kinetics studies of wild-type and D17N mutant ICH. Both the structure and kinetics are sensitive to the D17N mutation in a manner that is consistent with the order of the reaction steps seen in the simulations. Mobile protons play essential roles in many enzymes, yet they are difficult to observe experimentally, making the ordering of proton-dependent steps ambiguous in many enzyme mechanisms. The ability to directly simulate model reactions for the design of experiments that provide information about enzyme mechanisms involving mobile protons demonstrates the significance of our reactive QMD approach and motivates further biological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Vanishing dynamic strength measured in a transient high-pressure phase of tin

This work reports two independent experimental estimations of the dynamic strength of the body-centered tetragonal (bct) 𝛾 phase of tin, which occurs at pressures above about 9 GPa and transforms promptly back to the ambient 𝛽 phase upon pressure release. Measuring strength in such a transient high-pressure phase is challenging. One measurement used free-surface Richtmyer-Meshkov instabilities generated with gas gun impact. Another set of measurements used ramp-release loading in a pulsed-power facility. Strength estimations came from comparison to simulations using a comprehensive multiphase modeling framework that includes equation of state, shear moduli, and strength separately for each phase, and treats mixed-phase regions. Both experimental methods found the 𝛾-phase strength to be very low, within experimental uncertainty of zero. The existing literature on other materials, by contrast, almost universally reports higher strength in transient high-pressure phases compared with ambient phases. Recent Molecular-Dynamics simulations on tin in the literature showed almost zero deviatoric strength during a deformation-induced bct → bct transformation in which one of the 𝛾-phase 𝑎 axes “flips” to the 𝑐 axis. This reorientation currently provides the most plausible explanation for the low observed strength in 𝛾-phase tin.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Substrate-Directed Underlayer Growth of Bilayer MoS 2 Revealed by Mo Isotope Labeling

Direct control over the vertical formation sequence and stacking registry in van der Waals (vdW) bilayers is essential for device performance and moiré engineering yet difficult to resolve unambiguously with conventional probes. Here, we use Mo isotope labeling in a two-step chemical vapor deposition process to synthesize bilayer MoS 2 and trace its vertical formation on common substrates. By combining site-selective laser thinning, Raman spectroscopy, time-of-flight secondary ion mass spectrometry, and atomic-resolution scanning transimission electron microscopy (STEM), we find a clear substrate dependence: on SiO 2 /Si, the second layer nucleates and grows beneath the first (underlayer), whereas on sapphire, it forms on top (overlayer). Density functional theory indicates that a larger equilibrium interfacial separation and weaker MoS 2 –substrate interactions on amorphous SiO 2 permit confined interfacial diffusion and underlayer nucleation, whereas stronger interactions and smaller separations on sapphire favor overlayer growth. On SiO 2 , confined epitaxy templates commensurate 2H, 3R, and mixed bilayers, as confirmed by second harmonic generation spectroscopy and STEM. During underlayer coalescence, embedded mirror-twin grain boundaries stitch atomically sharp 2H|3R junctions via alternating 4|8 ring motifs. Molecular-dynamics simulations reveal that these alternating 4|8 motifs accommodate interlayer vdW coupling and locally modulate the stacking registry. These results provide mechanistic insight into confined epitaxial growth and establish isotope labeling as a powerful probe of two-dimensional materials synthesis.

MoS2↗
DOE OSTI2026

Phase instability-coupled fracture behavior in garnet LLZO solid electrolytes: a machine learning-enabled atomistic study

Fracture in the garnet-type solid electrolyte Li 7 La 3 Zr 2 O 12 (LLZO) poses a critical threat to both the performance and safety of solid-state batteries. To unravel the coupled chemomechanical processes that govern fracture in LLZO under external loading, we carry out large-scale molecular-dynamics simulations with a validated machine-learning force field. Our results demonstrate that triaxial stresses at crack flanks trigger a localized cubic-to-tetragonal phase transformation, which is accompanied by Li-ion rearrangement. The emergent tetragonal domains feature lattice contraction normal to the fracture plane, imposing coherent misfit strains that provide an additional driving force for further crack propagation. Crucially, introducing Li deficiencies stabilizes the cubic phase, postponing the phase transition and thereby delaying fracture initiation. These findings highlight the role of intrinsic phase instability in dictating LLZO's fracture resistance and its critical connection to local Li concentration. This chemomechanical coupling points toward targeted strategies to enhance the mechanical robustness of garnet electrolytes, including tuning Li content, ensuring dopant homogeneity, and refining processing protocols.

Materials science↗
DOE OSTI2025

High-pressure reduction of carbon dioxide in reactive liquid mixtures

Density-functional theory based molecular-dynamics simulations were used to investigate high-pressure chemical reactions in liquid mixtures of CO 2 with several elements (Si, Mn, and Fe) at high temperatures of 2000-3000 K. Our ab initio simulations indicate that these reactant elements can reduce CO 2 to C at high pressures (20 GPa) leading to the formation of C-C chains, with Si by far the most effective carbon-reducing agent. A combined chemical analysis using Bader charge analysis and Crystal Orbital Hamilton Population (COHP) on simulation snapshots shows that significant charge transfer from the reducing element to the C atoms creates instability in the C-O covalent bonds. COHP analysis further shows that Mn/Fe-O and Mn/Fe-C bonding interactions are weaker compared to the Si counterparts. These results further our understanding of the redox chemistry of CO 2 at conditions relevant to planetary mantle interiors and demonstrate the effectiveness of high pressure in the reduction of CO 2 directly to solid carbon.

Ab initio molecular dynamics↗
DOE OSTI2026

Shock compression of liquid helium to 360 GPa

Data for the shock equation of state of helium are obtained up to 360⁢G⁡Pa, nearly doubling the pressure of previous experimental measurements. The helium samples are first precompressed to 2.7 g⁡c⁢m −3 in a diamond anvil cell prior to laser-driven shock compression at the Omega Laser Facility. Time-resolved Doppler velocimetry and pyrometry reveal significant reflectivity and greater compressibility compared to the predictions of existing broad-range tabular equation of state models, which may be caused by the onset of ionization. These experimental observations, however, are largely captured with molecular-dynamics simulations based on density functional theory, affirming the ability of first-principles techniques to capture complex physics, while enabling critical insight for the behavior of warm dense helium in Jovian interiors and white dwarf atmospheres.

Physics - Plasma physics↗
DOE OSTI2026

High-pressure melt dynamics in shock-compressed titanium

In this work we study the high-pressure melting behavior of titanium using laser-driven shock compression with in situ femtosecond x-ray diffraction and molecular-dynamics simulations based on a machine-learned interatomic potential. The MD simulations predict the solid-liquid coexistence on the Hugoniot in the ∼111−124GPa range. Experimentally, we observe the first evidence of liquid at 86 GPa. We also observe pronounced microstructural changes with pressure, with strong grain refinement associated with the emergence of liquid, within the solid-liquid coexistence (∼110−126GPa). Above 126 GPa, we observe the persistence of residual levels of highly textured crystalline Ti to ∼180GPa, well above the expected melt completion pressure. We discuss the accuracy that current laser-shock experimental platforms have at determining the melt onset and completion pressures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Validated ligand geometries for macromolecular refinement restraints and molecular-mechanics force fields

In macromolecular structure refinement, the low observation-to-parameter ratio and the lack of high-resolution data are countered by using a priori information in the form of restraints. Having accurate geometries of the chemical entities in the sample is paramount for generating accurate chemical restraints and, therefore, accurate macromolecular structures. In particular, it is desirable to have accurate restraints for known and novel ligand entities. Quantum mechanics (QM) can minimize the energy of a ligand by adjusting its geometry, and these geometries can be used to generate restraints for macromolecular refinement. This article describes a library of approximately 37 000 small molecules extracted from the Chemical Component Dictionary in the Protein Data Bank and minimized by density-functional QM. The library includes restraint files for use in crystallography or cryo-EM refinement, along with files suitable for molecular-dynamics simulation. Because the geometries are validated using the Cambridge Structural Database, the restraints library provides users with both functional restraints and minimized geometries. This work also provides procedures for generating new and accurate restraints.

Amber↗
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↗
NASA NTRS1991

Dynamics of charged particles in a Paul radio-frequency quadrupole trap

A molecular-dynamics simulation of hundreds of ions confined in a Paul trap has been performed. The simulation includes the trapped particles' micromotion and interparticle Coulomb interactions. A random walk in velocity was implemented to bring the secular motion to a given temperature which was numerically measured. When the coupling Gamma is large the ions from concentric shells which undergo a quadrupole oscillation at the RF frequency, while the ions within a shell form a 2D hexagonal lattice. Ion clouds at 5 mK show no RF heating for q(z) less than about 0.6, whereas rapid heating is seen for qz = 0.8.

Prestage, J. D.↗
NASA NTRS1992

Terraced spreading of simple liquids on solid surfaces

We have studied the spreading of liquid drops on a solid surface by molecular-dynamics simulations of coexisting three-phase Lennard-Jones systems of liquid, vapor, and solid. We consider both spherically symmetric atoms and diatomic molecules, and a range of interaction strengths. As the attraction between liquid and solid increases we observe a smooth transition in spreading regimes, from partial to complete to terraced wetting. In the terraced case, where distinct monomolecular layers spread with different velocities, the layers are ordered but not solid, with substantial molecular diffusion both within and between layers. The quantitative behavior resembles recent experimental findings, but the detailed dynamics differ. In particular, the layers exhibit an unusual spreading law, where their radii vary in time as R-squared approximately equal to log10t, which disagrees with experiments on polymeric liquids as well as recent calculations.

Yang, Ju-Xing↗
DOE OSTI2026

Primary defect production from molecular dynamics simulations of high-energy displacement cascades in NbMoTaW alloys

In this work, we report on large-scale molecular-dynamics (MD) simulations of displacement cascades in equiatomic NbMoTaW alloys at PKA energies ranging from 0.15 to 150 keV. We find defect production to be strongly dependent on recoil energy, scaling sublinearly up to 10 keV, and linearly thereafter. We find the sublinear regime to be defined by low values of surviving Frenkel pairs, typically found as isolated point defects or small defect clusters, while at higher recoil energies dense cascades become more frequent, leading to splitting into subcascades and the production of relatively large prismatic-dislocation loops with ⟨111⟩ and ⟨001⟩ Burgers vectors. These loops immobilize large fractions of defects, leading to a rapid growth of the number of surviving defects in the linear regime. We also anneal post-cascade defect configurations using object-kinetic Monte Carlo (OKMC) simulations to account for intracascade recombination on time scales not accessible to MD simulations. Cascade annealing is strongly temperature dependent, with the OKMC simulations only showing significant recovery at 1000 K but not below. Our results are in general agreement with existing published data for refractory concentrated alloys.

Zhou, Xinran [Lawrence Berkeley National Laborator↗
DOE OSTI2025

Nonequilibrium effects in high-gain inertial confinement fusion

Recent experimental demonstrations of ignition and target gain in inertial confinement fusion (ICF) have stimulated interest in exploring the fundamental physics of violent deuterium-tritium (DT) burn in high-gain ICF targets. A significant DT-burn fraction is a necessary condition for high energy gain and large neutron yields (>100MJ). Using classical molecular-dynamics (MD) simulations and a hybrid fluid-kinetic model, we examine how a large fraction of low-energy 𝛼 particles can kick D and T ions out of equilibrium in high-gain ICF targets. The MD results suggest that (1) temperatures of 𝑇 𝐷 and 𝑇 𝑇 can differ by as much as ∼20% of their mean temperature and (2) the deviation of the DT energy distribution from the Maxwell-Boltzmann function can exceed ∼30%. Some of these MD observations, such as the preferential heating of D ions by low-energy 𝛼 particles and the temperature separation, can be explained by a proposed hybrid fluid-kinetic model. Furthermore, the implication of such nonequilibrium effects on the DT reactivity is also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Efficient machine learning interatomic potentials robust for liquid and multiple solid polymorphs of NaF and KF

Achieving atomic-level understanding of crystallization of molten salts is of importance to a wide range of technological applications. Recent work [Fan et al., Proc. Natl. Acad. Sci. USA 122, e2425702122 (2025)] revealed that crystal nucleation in molten LiF salt is a multistage process according to the molecular-dynamics (MD) simulations based on an atomic cluster expansion (ACE) machine-learning interatomic potential (MLIP). In order to understand the influence of increasing cation size on nucleation pathways and nucleation rates of molten fluoride salts, here we develop two new ACE MLIPs for NaF and KF. The two ACE MLIPs feature DFT-SCAN-level accuracy for liquid and multiple solid polymorphs over a wide temperature (0–2000 K) and pressure (0–100 GPa) range, and also reproduce well a number of experimental data for solid and liquid equilibrium properties. The efficiency of the two ACE MLIPs enable million-atom-scale or microsecond-scale MD simulations. The two general-purpose ACE MLIPs are expected to be useful for atomistic simulations for different purposes, in addition to studying crystallization of molten NaF and KF salts.

Crystal melting↗
NASA NTRS1985

Laplace-transform technique for deriving thermodynamic equations from the classical microcanonical ensemble

A direct and convenient method is presented for deriving expressions which equate any thermodynamic state function to averages of specific dynamical functions and their fluctuations over the classical microcanonical distribution. Specific expressions are obtained for a variety of thermodynamic quantities. The effect of various entropy definitions on the results are assessed, and the latter are compared to previous work in the literature. The derived formulas are applied to the analysis of molecular-dynamics computer simulations.

Pearson, E. M.↗
DOE OSTI2026

Elucidating Lithium Transport Mechanisms in Disordered LiF from Machine-Learning Molecular Dynamics Simulations

Lithium fluoride (LiF) is a ubiquitous component of solid- and cathode–electrolyte interphases, yet its functional role remains unclear under the structural and chemical heterogeneity typical of cycling batteries. Here, we systematically quantify how structural disorder, off-stoichiometry, and strain govern Li-ion transport in LiF. Using a machine-learning potential to enable extensive molecular-dynamics sampling, we compare crystalline and amorphous LiF, Li 0.95 F, and LiF 0.95 , and evaluate the impact of small homogeneous deformations. Defect-free crystalline LiF is effectively ion-blocking at 300−500 K, whereas amorphization generates free-volume–assisted percolation pathways that facilitates Li-ion diffusion. At elevated temperatures, thermodynamically driven crystallization disrupts these pathways, leading to non-Arrhenius behavior. In crystalline phases, Li deficiency activates vacancy-mediated diffusion, while in amorphous LiF, transport is governed primarily by network connectivity. Strain is found to have only a marginal effect on Li mobility in both crystalline and amorphous structures.

Batteries↗
DOE OSTI2026

Kinetic Monte Carlo Framework for Coupled Degradation and Dehydration of Anion Exchange Membranes

Kinetic Monte Carlo (kMC) simulations, augmented with temporal-acceleration schemes, can efficiently handle stiff reaction-transport networks when fast processes rapidly relax to quasi-equilibrium on a fixed lattice. However, in glassy anion-exchange membranes (AEM), rare and irreversible chemical degradation events continuously reshape the nanoscale morphology, and the associated hydration and transport degrees of freedom remain far from a well-defined local equilibrium. This combination of evolving state space and nonequilibrated fast dynamics lies outside the scope of existing kMC acceleration frameworks. Here, to address this challenge, we introduce an auxiliary-particle kinetic Monte Carlo (AP-kMC) scheme. In AP-kMC, short-lived mobile particles spawned at degradation sites execute hop, water-elimination, and decay moves, enforcing rapid local relaxation of the hydration structure while preserving the stochastic rules of kMC. Parameterized with molecular-dynamics morphologies and experimental solution degradation kinetics, AP-kMC reproduces the evolution of ion-exchange capacity, water uptake, and conductivity, and reveals a feedback loop in which poorly hydrated sites degrade first and each degradation event induces further local dehydration. The resulting thinning and fragmentation of water channels cause loss of hydrophilic percolation and abrupt conductivity collapse well before complete charge loss. AP-kMC thus reframes AEM durability as a coupled degradation–drying–percolation problem and provides a transferable strategy to simulate reactive, out-of-equilibrium polymer electrolytes where local solvation controls reactivity.

organic↗