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Metal Hydroxide and Oxyhydroxide Gas Database

In a high temperature combustion situation such as a gas turbine or rocket engine, combustion products include water vapor. This water vapor reacts with oxides in the hot-gas-path to form volatile hydroxides and oxyhydroxides. The oxides may be structural oxide components or protective oxides on alloys or non-oxide ceramics. In either case, this enhanced volatilization leads to degradation of components. Reliable thermochemical data for many metal hydroxides and oxyhydroxides has not been available. We review a database put together at NASA over many years based on experimental and ab initio computations for thermochemical data of Al, Si, Ti, Cr, Mn, Y, Zr, La, Ta, Gd, and Yb hydroxides and oxyhydroxides. Experimental methods for measuring thermodynamic quantities of metal hydroxides and oxyhydroxides are limited since the required high temperatures and high pressures preclude many conventional methods. Nonetheless, transpiration has proven a valuable tool in our laboratories for the Si-O-H, Cr-O-H, and Ti-O-H systems. Data are analyzed with second and third law methods. These data can be leveraged with quantum chemistry to provide reliable free energy functions for the third law calculation and comparison to the derived enthalpies of formation. Both wave function and DFT methods can be utilized for deriving the thermodynamic functions for the various metal hydroxides and oxyhydroxides. We have found that, in general, reasonable molecular geometries and spectroscopic data can be derived with a relatively low level of theory, i.e. DFT with the B3LYP functional. Accurate calculations of the enthalpy of formation require higher levels of theory, such as the Coupled Cluster, Singles, Doubles, and perturbative Triples CCSD(T) approach with complete basis sets (CBS). Also, corrections for hindered rotations and anharmonicity are necessary. For some symmetries, the anharmonic correction has proven to be a challenge and we have found that this is only treated properly by certain codes. The database lists the fHo(298), So(298), and Cp in the form Cp = A + BT + CT2 + D/T + E/T2, where A, B, C, D, and E are the fitting constants. Thus, it can be easily used in any of the common computational thermochemistry codes. To date, the database contains 51 gaseous species of the formula MOx(OH)y.

Metal Hydroxides↗
DOE OSTI2016

Substituent effects on dynamics at conical intersections: Allene and methyl allenes

We report a joint experimental and theoretical study on the ultrafast excited state dynamics of allene and a series of its methylated analogues (1,2-butadiene, 1,1-dimethylallene, and tetramethylallene) in order to elucidate the conical intersection mediated dynamics that give rise to ultrafast relaxation to the ground electronic state. We use femtosecond time-resolved photoelectron spectroscopy (TRPES) to probe the coupled electronic-vibrational dynamics following UV excitation at 200 nm (6.2 eV). Ab initio multiple spawning (AIMS) simulations are employed to determine the mechanistic details of two competing dynamical pathways to the ground electronic state. In all molecules, these pathways are found to involve as follows: (i) twisting about the central allenic C–C–C axis followed by pyramidalization at one of the terminal carbon atoms and (ii) bending of allene moiety. Importantly, the AIMS trajectory data were used for ab initio simulations of the TRPES, permitting direct comparison with experiment. For each molecule, the decay of the TRPES signal is characterized by short (30 fs, 52 fs, 23 fs) and long (1.8 ps, 3.5 ps, [306 fs, 18 ps]) time constants for 1,2-butadiene, 1,1-dimethylallene, and tetramethylallene, respectively. However, AIMS simulations show that these time constants are only loosely related to the evolution of electronic character and actually more closely correlate to large amplitude motions on the electronic excited state, modulating the instantaneous vertical ionization potentials. Furthermore, the fully substituted tetramethylallene is observed to undergo qualitatively different dynamics, as displacements involving the relatively massive methyl groups impede direct access to the conical intersections which give rise to the ultrafast relaxation dynamics observed in the other species. These results show that the branching between the “twisting” and “bending” pathways can be modified via the selective methylation of the terminal carbon atoms of allene. The interplay between inertial and potential effects is a key to understanding these dynamical branching pathways. The good agreement between the simulated and measured TRPES confers additional confidence to the dynamical picture presented here.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

DEVELOPMENT OF INTERATOMIC POTENTIALS IN TUNGSTEN-RHENIUM SYSTEMS

Reference data are generated using the ab initio method to fit interatomic potentials for the W-Re system. The reference data include single phases of W and Re, strained structures, slabs, systems containing several concentrations of vacancies, systems containing various types of interstitial defects, melt structures, structures in the σ and χ phases, and structures containing several concentrations of solid solutions of Re in bcc W and W in hcp Re. Future work will start the fitting iterations.

Density Functional Theory↗
DOE OSTI2023

An autonomous laboratory for the accelerated synthesis of novel materials

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deciphering the Solvation Structure of Aqueous ZnCl 2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network

Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl 2 solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, “water-in-salt”) regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. Finally, this work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.

25 ENERGY STORAGE↗
DOE OSTI2019

Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry

Because collecting precise and accurate chemistry data is often challenging, chemistry data sets usually only span a small region of chemical space, which limits the performance and the scope of applicability of data-driven models. To address this issue, we integrated an active learning machine with automatic ab initio calculations to form a self-evolving model that can continuously adapt to new species appointed by the users. In the present work, we demonstrate the self-evolving concept by modeling the formation enthalpies of stable closed-shell polycyclic species calculated at the B3LYP/6-31G(2df,p) level of theory. By combining a molecular graph convolutional neural network with a dropout training strategy, the model we developed can predict density functional theory (DFT) enthalpies for a broad range of polycyclic species and assess the quality of each predicted value. For the species which the current model is uncertain about, the automatic ab initio calculations provide additional training data to improve the performance of the model. For a test set composed of 2858 cyclic and polycyclic hydrocarbons and oxygenates, the enthalpies predicted by the model agree with the reference DFT values with a root-mean-square error of 2.62 kcal/mol. Finally, we found that a model originally trained on hydrocarbons and oxygenates can broaden its prediction coverage to nitrogen-containing species via an active learning process, suggesting that the continuous learning strategy is not only able to improve the model accuracy but is also capable of expanding the predictive capacity of a model to unseen species domains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
DOE OSTI2023

Dynamic Monkey Bar Mechanism of Superionic Li–ion Transport in LiTaCl 6

Here, the LiTaCl 6 solid electrolyte has the lowest activation energy of ionic conduction at ambient conditions (0.165 eV), with a record high ionic conductivity for a ternary compound (11 mS cm –1 ). However, the mechanism has been unclear. We train machine-learning force fields (MLFF) on ab initio molecular dynamics (AIMD) data on-the-fly and perform MLFF MD simulations of AIMD quality up to the nanosecond scale at the experimental temperatures, which allows us to predict accurate activation energy for Li-ion diffusion (at 0.164 eV). Detailed analyses of trajectories and vibrational density of states show that the large-amplitude vibrations of Cl – ions in TaCl 6 – enable the fast Li-ion transport by allowing dynamic breaking and reforming of Li–Cl bonds across the space in between the TaCl 6 – octahedra. We term this process the dynamic-monkey-bar mechanism of superionic Li + transport which could aid the development of new solid electrolytes for all-solid-state lithium batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

A = 3 ( e , e ' ) x B ≥ 1 cross-section ratios and the isospin structure of short-range correlations

We study the relation between measured high-x B , high-Q 2 , helium-3 to tritium, (e,e') inclusive-scattering cross-section ratios and the relative abundance of high-momentum neutron-proton (np) and proton-proton (pp) short-range correlated (SRC) nucleon pairs in three-body (A=3) nuclei. In this study, analysis of this data using a simple pair-counting cross-section model suggested a much smaller np/pp ratio than previously measured in heavier nuclei, questioning our understanding of A=3 nuclei and, by extension, all other nuclei. Here we examine this finding using spectral-function-based cross-section calculations, with both an ab initio A=3 spectral function and effective Generalized Contact Formalism (GCF) spectral functions using different nucleon-nucleon interaction models. The ab initio calculation agrees with the data, showing good understanding of the structure of A=3 nuclei. An 8% uncertainty on the simple pair-counting model, as implied by the difference between it and the ab initio calculation, gives a factor of 5 uncertainty in the extracted np/pp ratio. Thus we see no evidence for the claimed "unexpected structure in the high-momentum wavefunction for hydrogen-3 and helium-3."

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dynamic Monkey Bar Mechanism of Superionic Li‐ion Transport in LiTaCl 6

Abstract The LiTaCl 6 solid electrolyte has the lowest activation energy of ionic conduction at ambient conditions (0.165 eV), with a record high ionic conductivity for a ternary compound (11 mS cm −1 ). However, the mechanism has been unclear. We train machine‐learning force fields (MLFF) on ab initio molecular dynamics (AIMD) data on‐the‐fly and perform MLFF MD simulations of AIMD quality up to the nanosecond scale at the experimental temperatures, which allows us to predict accurate activation energy for Li‐ion diffusion (at 0.164 eV). Detailed analyses of trajectories and vibrational density of states show that the large‐amplitude vibrations of Cl − ions in TaCl 6 − enable the fast Li‐ion transport by allowing dynamic breaking and reforming of Li−Cl bonds across the space in between the TaCl 6 − octahedra. We term this process the dynamic‐monkey‐bar mechanism of superionic Li + transport which could aid the development of new solid electrolytes for all‐solid‐state lithium batteries.

Lei, Ming↗
DOE OSTI2025

Mobility assessment of the BCC and carbide phases in the C-Nb, C-U and Nb-U systems

Uranium carbides with refractory metal additions are considered for Gen IV nuclear reactors and nuclear thermal propulsion as fuels for their high-temperature and corrosion resistant properties. Understanding kinetic effects that dictate microstructural evolution during fabrication and operating conditions is essential to advance technological development of these fuels. This work presents the development of an atomic mobility database for C-Nb-U systems based off available experimental data supported with ab-initio methods. The mobility assessments and uncertainty quantification (using Markov chain Monte Carlo) were conducted in the Kawin software. Carbon diffusion is considered dominant, as metal diffusion is much slower, with niobium diffusion being even slower and rate limiting than uranium metal. We provide a comprehensive and self-consistent thermo-kinetic database that is validated by diffusion couple simulations through Kawin. In conclusion, this enables prediction of microstructural and phase evolution critical for the development and lifetime assessment of next generation nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Synthesis, structure and canted antiferromagnetism of layered cobalt hydroxide sorbate

Layered hybrid organic–inorganic compounds of the transition metal hydroxides, in which part of the hydroxide ions are exchanged for organic ligands that act as spacers of the 2-dimensional inorganic layers, elicit new or enhanced properties. The interlayer ligands enable fine-tuning of the physicochemical properties of the metal hydroxide layers, including their magnetism. Specifically, within the Co(OH) 2 family of transition ion hydroxides, this approach has been leveraged to stabilize antiferromagnetic and ferrimagnetic ground states for pillared and layered complexes, respectively. Here, in this work, we further investigate the effects of changing dimensionality, local Co coordination, and interlayer spacing on the magnetic ground state of brucite-like β-Co(OH) layered complexes. We report the hydrothermal synthesis of a new crystalline material, Co(OH)(sorb), where the inorganic layers are spaced by monotopic, doubly unsaturated sorbate ligands (sorb = [C 6 H 7 O 2 ] – ). The material was structurally characterized using powder X-ray and neutron diffraction measurements and the crystal structure was solved ab initio from powder diffraction data and refined using the Rietveld method. The magnetic susceptibility and magnetization measurements reveal canted antiferromagnetic ordering with T N = 41.7 K. This report augments our understanding of tuning magnetism through dimensionality in layered complexes and represents a step forward in the design of new 2D layered hybrid compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Meso-scale anisotropic hydrogen segregation near grain-boundaries in polycrystalline nickel characterized by EBSD/SIMS

To study anisotropic hydrogen segregation and diffusion in nickel polycrystalline, Secondary Ion Mass Spectrometry (SIMS) and Electron Back Scattered Diffraction (EBSD) are integrated to investigate hydrogen distribution around grain boundaries. Hydrogen distribution in pre-charged samples were correlated with grain boundary character by integrating high-resolution grain microstructure from EBSD inverse pole figure map and low-resolution hydrogen concentration profile map from SIMS. This multimodal imaging instrumentation shows that grain boundaries in nickel can be categorized into two families based on behavior of hydrogen distribution crossing grain boundary: the first one includes random grain boundaries with fast hydrogen diffusivity, showing a sharp gap for hydrogen concentration profile cross the grain boundaries. The second family are special Σ3n grain boundaries with low hydrogen diffusivity, showing a smooth gradient of hydrogen concentration cross the grain boundary. Heterogeneous hydrogen distributions due to grain boundary family revealed by SIMS/EBSD on mesoscale further validate the recent hydrogen permeation data and anisotropic ab-initio calculations in nanoscale. The results highlight the fact that grain boundaries character impacts hydrogen distribution significantly.

Electron Back Scattered Diffraction↗
DOE OSTI2016

An Atomistic View of the Incipient Growth of Zinc Oxide Nanolayers

The growth of zinc oxide thin films by atomic layer deposition is believed to proceed through an embryonic step in which three-dimensional nanoislands form and then coalesce to trigger a layer-by-layer growth mode. This transient initial state is characterized by a poorly ordered atomic structure, which may be inaccessible by X-ray diffraction techniques. Here in this work, we apply X-ray absorption spectroscopy in situ to address the local structure of Zn after each atomic layer deposition cycle, using a custom-built reactor mounted at a synchrotron beamline, and we shed light on the atomistic mechanisms taking place during the first stages of the growth. We find that such mechanisms are surprisingly different for zinc oxide growth on amorphous (silica) and crystalline (sapphire) substrate. Ab initio simulations and quantitative data analysis allow the formulation of a comprehensive growth model, based on the different effects of surface atoms and grain boundaries in the nanoscale islands, and the consequent induced local disorder. From a comparison of these spectroscopy results with those from X-ray diffraction reported recently, we observe that the final structure of the zinc oxide nanolayers depends strongly on the mechanisms taking place during the initial stages of growth. Finally, the approach followed here for the case of zinc oxide will be of general interest for characterizing and optimizing the growth and properties of more complex nanostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Polymorphism in Strontium Tungstate SrWO 4 under Quasi-Hydrostatic Compression

The structural and vibrational properties of SrWO 4 have been studied experimentally up to 27 and 46 GPa, respectively, by angle-dispersive synchrotron X-ray diffraction and Raman spectroscopy measurements as well as using ab initio calculations. The existence of four polymorphs upon quasi-hydrostatic compression is reported. The three phase transitions were found at 11.5, 19.0, and 39.5 GPa. The ambient-pressure SrWO 4 tetragonal scheelite-type structure (S.G. I4 1 /a) undergoes a transition to a monoclinic fergusonite-type structure (S.G. I2/a) at 11.5 GPa with a 1.5% volume decrease. Subsequently, at 19.0 GPa, another structural transformation takes place. Our calculations indicate two possible post-fergusonite phases, one monoclinic and the other orthorhombic. In the diffraction experiments, we observed the theoretically predicted monoclinic LaTaO 4 -type phase coexisting with the fergusonite-type phase up to 27 GPa. The coexistence of the two phases and the large volume collapse at the transition confirm a kinetic hindrance typical of first-order phase transitions. Significant changes in Raman spectra suggest a third pressure-induced transition at 39.5 GPa. The conclusions extracted from the experiments are complemented and supported by ab initio calculations. Furthermore, our data provides insight into the structural mechanism of the first transition, with the formation of two additional W–O contacts. The fergusonite-type phase can be therefore considered as a structural bridge between the scheelite structure, composed of [WO 4 ] tetrahedra, and the new higher pressure phases, which contain [WO 6 ] octahedra. All the observed phases are compatible with the high-pressure structural systematics predicted for ABO 4 compounds using crystal-chemistry arguments such as the diagram proposed by Bastide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗