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Results for “Crystal structure identification”

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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ChemEnv : a fast and robust coordination environment identification tool

Coordination or local environments have been used to describe, analyze and understand crystal structures for more than a century. Here, a new tool called ChemEnv, which can identify coordination environments in a fast and robust manner, is presented. In contrast to previous tools, the assessment of the coordination environments is not biased by small distortions of the crystal structure. Its robust and fast implementation enables the analysis of large databases of structures. The code is available open source within the pymatgen package and the software can also be used through a web app available on http://crystaltoolkit.org through the Materials Project.

42 ENGINEERING↗
DOE OSTI2020

Automated Identification of Molecular Crystals’ Packing Motifs

Packing motifs—patterns in how molecules orient relative to one another in a crystal structure—are an important concept in many subdisciplines of materials science because of correlations observed between specific packing motifs and properties of interest. That said, packing motif data sets have remained small and noisy due to intensive manual labeling processes and insufficient labeling schemes. The most prominent labeling algorithms calculate relative interplanar angles of nearest neighbor molecules to determine the packing motif of a molecular crystal, but this simple approach can fail when neighbors are naively sampled isotropically around the crystal structure. To remedy this issue, here we propose an optimization algorithm, which rotates the molecular crystal structure to find representative molecules that inform the packing motif. We package this algorithm into an automated framework—Autopack—which both optimally rotates the crystal structure and labels the packing motif based on the appropriate neighboring molecules. In this work, we detail the Autopack framework and its performance, which shows improvements compared to previous state-of-the-art labeling methods, providing the first quantitative point of comparison for packing motif labeling algorithms. Furthermore, using Autopack (available at https://ipo.llnl.gov/technologies/software/autopack), we perform the first large-scale study of potential relationships between chemicals’ compositions and packing motifs, which shows that these relationships are more complex than previously hypothesized from studies that used only tens of polycyclic aromatic hydrocarbon molecules. Autopack’s capabilities help pose next steps for crystal engineering research focusing not only on a molecule’s adoption of a specific packing motif but also on new structure–property relationships.

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

LeaPP: Learning Pathways to Polymorphs through Machine Learning Analysis of Atomic Trajectories

Understanding the mechanisms underlying crystal nucleation and growth is crucial for many technological applications. Due to the short length and time scales involved, crystal nucleation is often studied using molecular simulations. Most existing approaches to extract the nucleation mechanism from simulations focus on the analysis of static snapshots of the configurations, potentially overlooking subtle local fluctuations and the history of the particles involved in the formation of solid nuclei. Here, in this work, we propose a novel methodology called LeaPP that categorizes nucleation trajectories based on the temporal information of their constituent particles. We leverage the time evolution of the local environment of the crystallizing particles to encapsulate the relationship between the structure and dynamics and distinguish between different evolving particle paths. Identification of the distinct particle paths further enables characterizing the nucleation trajectories into different pathways. Collectively, LeaPP provides a more nuanced understanding of nucleation through an unsupervised approach with lesser dependence on traditional order parameters. Furthermore, the pathways identified by LeaPP are predictive of the resulting polymorph. We demonstrate LeaPP on three different systems─Lennard-Jones-like particles, Ni 3 Al, and water on surfaces. The general methodology underlying LeaPP─considering the time evolution of the building blocks─applies to a wide range of self-assembly problems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probing growth of metal–organic frameworks with X-ray scattering and vibrational spectroscopy

Nucleation and crystallization arising from liquid to solid phase are involved in a multitude of processes in fields ranging from materials science to biology. Controlling the thermodynamics and kinetics of growth is advantageous to help tune the formation of complex morphologies. In this report, we harness wide-angle X-ray scattering and vibrational spectroscopy to elucidate the mechanism for crystallization and growth of the metal–organic framework Co-MOF-74 within microscopic volumes enclosed in a capillary and an attenuated total reflection microchip reactor. The experiments reveal molecular and structural details of the growth processes, while the results of plane wave density functional calculations allow identification of lattice and linker modes in the formed crystals. Synthesis of the metal–organic framework with microscopic volumes leads to monodisperse and micron-sized crystals, in contrast to those typically observed under bulk reaction conditions. Reduction in the volume of reagents within the microchip reactor was found to accelerate the reaction rate. The coupling of spectroscopy with scattering to probe reactions in microscopic volumes promises to be a useful tool in the synthetic chemist's kit to understand chemical bonding and has potential in designing complex materials.

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

Robust design of semi-automated clustering models for 4D-STEM datasets

Materials discovery and design require characterizing material structures at the nanometer and sub-nanometer scale. Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM) resolves the crystal structure of materials, but many 4D-STEM data analysis pipelines are not suited for the identification of anomalous and unexpected structures. This work introduces improvements to the iterative Non-Negative Matrix Factorization (NMF) method by implementing consensus clustering for ensemble learning. We evaluate the performance of models during parameter tuning and find that consensus clustering improves performance in all cases and is able to recover specific grains missed by the best performing model in the ensemble. The methods introduced in this work can be applied broadly to materials characterization datasets to aid in the design of new materials.

Bruefach, Alexandra (ORCID:0000000209323477)↗
DOE OSTI2020

In Situ Oxygen Isotope Determination in Serpentine Minerals by SIMS: Addressing Matrix Effects and Providing New Insights on Serpentinisation at Hole BA1B (Samail ophiolite, Oman)

The ability to constrain the petrogenesis of multiple serpentine generations recorded at the microscale is crucial for estimating the extent and conditions of modern versus fossil serpentinisation in ophiolites. To address matrix bias effects during oxygen isotope analysis by SIMS, we present the first investigation analysing antigorite in the compositional range Mg# = 77.5–99.5 mole %, using a CAMECA IMS–1280 secondary ion mass spectrometer. Spot–to–spot homogeneity is ≤ 0.5‰ (2s) for the new antigorite reference materials. The relative bias between antigorite reference materials with different Mg/Fe ratios is described by a second order polynomial, and a maximum difference in bias of ~ 1.8‰ was measured for Mg# ~ 78 to 100. We observed a bias up to ~ 1.0‰ between lizardite and antigorite attributed to their different crystal structure. Orientation effects up to ~ 1‰ were observed in chrysotile. The new analytical protocol allowed the identification of oxygen isotope zoning up to ~ 7‰ in serpentine minerals from two serpentinites recovered from an area of active serpentinisation in the Samail ophiolite. Furthermore in situ analysis is capable of resolving isotopic heterogeneity that may directly reflect changes in the physical and chemical conditions of multiple serpentinisation events in the Samail ophiolite.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Score-based denoising for atomic structure identification

We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods, such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural and Enzymatic Analyses Reveal the Binding Mode of a Novel Series of Francisella tularensis Enoyl Reductase (FabI) Inhibitors

Because of structural and mechanistic differences between eukaryotic and prokaryotic fatty acid synthesis enzymes, the bacterial pathway, FAS-II, is an attractive target for the design of antimicrobial agents. We have previously reported the identification of a novel series of benzimidazole compounds with particularly good antibacterial effect against Francisella tularensis, a Category A biowarfare pathogen. Herein we report the crystal structure of the F. tularensis FabI enzyme in complex with our most active benzimidazole compound bound with NADH. The structure reveals that the benzimidazole compounds bind to the substrate site in a unique conformation that is distinct from the binding motif of other known FabI inhibitors. Detailed inhibition kinetics have confirmed that the compounds possess a novel inhibitory mechanism that is unique among known FabI inhibitors. These studies could have a strong impact on future antimicrobial design efforts and may reveal new avenues for the design of FAS-II active antibacterial compounds.

Mehboob, Shahila↗
DOE OSTI2021

AlphaFold Protein Structure Database for Sequence-Independent Molecular Replacement

Crystallographic phasing recovers the phase information that is lost during a diffraction experiment. Molecular replacement is a commonly used phasing method for crystal structures in the protein data bank. In one form it uses a protein sequence to search a structure database to find suitable templates for phasing. However, sequence information is not always available, such as when proteins are crystallized with unknown binding partner proteins or when the crystal is of a contaminant. The recent development of AlphaFold published the predicted protein structures for every protein from twenty distinct species. In this work, we tested whether AlphaFold-predicted E. coli protein structures were accurate enough to enable sequence-independent phasing of diffraction data from two crystallization contaminants of unknown sequence. Using each of more than 4000 predicted structures as a search model, robust molecular replacement solutions were obtained, which allowed the identification and structure determination of YncE and YadF. Our results demonstrate the general utility of the AlphaFold-predicted structure database with respect to sequence-independent crystallographic phasing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Complementarity of neutron, XFEL and synchrotron crystallography for defining the structures of metalloenzymes at room temperature

Room-temperature macromolecular crystallography allows protein structures to be determined under close-to-physiological conditions, permits dynamic freedom in protein motions and enables time-resolved studies. In the case of metalloenzymes that are highly sensitive to radiation damage, such room-temperature experiments can present challenges, including increased rates of X-ray reduction of metal centres and site-specific radiation-damage artefacts, as well as in devising appropriate sample-delivery and data-collection methods. It can also be problematic to compare structures measured using different crystal sizes and light sources. In this study, structures of a multifunctional globin, dehaloperoxidase B (DHP-B), obtained using several methods of room-temperature crystallographic structure determination are described and compared. Here, data were measured from large single crystals and multiple microcrystals using neutrons, X-ray free-electron laser pulses, monochromatic synchrotron radiation and polychromatic (Laue) radiation light sources. These approaches span a range of 18 orders of magnitude in measurement time per diffraction pattern and four orders of magnitude in crystal volume. The first room-temperature neutron structures of DHP-B are also presented, allowing the explicit identification of the hydrogen positions. The neutron data proved to be complementary to the serial femtosecond crystallography data, with both methods providing structures free of the effects of X-ray radiation damage when compared with standard cryo-crystallography. Comparison of these room-temperature methods demonstrated the large differences in sample requirements, data-collection time and the potential for radiation damage between them. With regard to the structure and function of DHP-B, despite the results being partly limited by differences in the underlying structures, new information was gained on the protonation states of active-site residues which may guide future studies of DHP-B.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Robust error calibration for serial crystallography

Serial crystallography is an important technique with unique abilities to resolve enzymatic transition states, minimize radiation damage to sensitive metalloenzymes and perform de novo structure determination from micrometre-sized crystals. This technique requires the merging of data from thousands of crystals, making manual identification of errant crystals unfeasible. cctbx.xfel.merge uses filtering to remove problematic data. However, this process is imperfect, and data reduction must be robust to outliers. We add robustness to cctbx.xfel.merge at the step of uncertainty determination for reflection intensities. This step is a critical point for robustness because it is the first step where the data sets are considered as a whole, as opposed to individual lattices. Robustness is conferred by reformulating the error-calibration procedure to have fewer and less stringent statistical assumptions and incorporating the ability to down-weight low-quality lattices. We then apply this method to five macromolecular XFEL data sets and observe the improvements to each. The appropriateness of the intensity uncertainties is demonstrated through internal consistency. This is performed through theoretical CC 1/2 and I /σ relationships and by weighted second moments, which use Wilson's prior to connect intensity uncertainties with their expected distribution. This work presents new mathematical tools to analyze intensity statistics and demonstrates their effectiveness through the often underappreciated process of uncertainty analysis.

Mittan-Moreau, David W.↗
DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗
DOE OSTI2020

rlmolecule: A library for general-purpose material and molecular optimization using AlphaZero-style reinforcement learning

This library includes in-progress code for the optimization of materials and molecules using reinforcement learning. This project seeks to develop a generalized machine learning approach for optimizing targeted, and often complex, functional properties over the space of possible material structures, allowing faster identification of promising candidates. Electronic and transport properties of materials are determined by their molecular structure (for molecules) or their crystal structure (for in-organic crystalline materials). The atomic composition, bonding, and 3-D-spatial arrangement in these systems therefore represent a vast, discontinuous search space for optimal candidates. This library implements AlphaZero style reinforcement learning applied to two worked examples: the optimization of stable organic radicals for redox flow batteries, and the determination of stable electrolyte species for solid state batteries. The current code contains proof-of-concept results run on the Eagle HPC system, while the completed package will be a flexible tool for material optimization across of number of energy-relevant fields, which leverages cloud compute resources for the majority of the computational heavy lifting.

Biagioni, David↗
DOE OSTI2023

Surface structure studies in 2D and 3D Nb resonators using GI-XRD

Superconductor radio frequency (SRF) Nb-resonators are a key element in the development of new generations of particle accelerators as well as in the fabrication of 3D circuit QED architecture for quantum computing. Nevertheless, Niobium is extremally reactive to light elements such as C, N, O and H, and therefore to the impurities ordering under special conditions, e.g., cryogenic temperatures. Since these resonators are put through a series of metallurgical and chemical processes, the number of impurities in the solid increases in tens of ppm. Upon operational conditions ~1.6 K, Nb become vulnerable to H atoms ordering, which leads to the nucleation of secondary phases such as Nb-hydrides. Thereupon to the energy dissipation and eventually to the superconductivity breakdown known as Q-disease and potentially High-Field Q-slope. In this contribution, we present a detailed structural analysis by high-energy grazing-incidence X-ray diffraction of specimens extracted from 3D Nb resonators to shed light on the kinetic formation of the resulting secondary phases and their crystal phase identification upon cooling and heating cycles. To our knowledge, this is the first study carried out in resonators samples using a light source, shallow angles and temperatures near ~4 K. Consequently, this work opens new routes to understand the chemical and phase composition, crystal and electronic structure of the Nb surface at temperatures near the operating conditions as a strategy to improve the physical and functional properties of Nb superconducting resonators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Synthesis of magnesiowüstite nanocrystallites embedded in an amorphous silicate matrix via low energy multiple ion implantations

The synthesis process is presented for experimentally simulating modifications in cosmic dust grains using sequential ion implantations or irradiations followed by thermal annealing. Cosmic silicate dust analogues were prepared via implantation of 20–80 keV Fe - , Mg - , and O - ions into commercially available p-type silicon (100)wafers. The as-implanted analogues are amorphous with a Mg/(Fe+Mg) ratio of 0.5 tailored to match theoretical abundances in circumstellar dusts. Before the ion implantations were performed, Monte-Carlo-based ion-solid interaction codes were used to model the dynamic redistribution of the implanted atoms in the silicon substrate. 600 keV helium ion irradiation was performed on one of the samples before thermal annealing. Two samples were thermally annealed at a temperature appropriate for an M-class stellar wind, 1000 K, for 8.3 h in a vacuum chamber with a pressure of 1 x 10 -7 torr. The elemental depth profiles were extracted utilizing Rutherford Backscattering Spectrometry (RBS) in the samples before and after thermal annealing. X-ray diffraction (XRD)analysis was employed for the identification of various phases in crystalline minerals in the annealed analogues. Transmission electron microscopy (TEM) analysis was utilized to identify specific crystal structures. RBS analysis shows redistribution of the implanted Fe, Mg, and O after thermal annealing due to incorporation into the crystal structures for each sample type. XRD patterns along with TEM analysis showed nanocrystalline Mg and Fe oxides with possible incorporation of additional silicate minerals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Theoretical examination of nuclear spin diffusion in light-induced spin coherences in photosystem I

Microsecond coherence times are predicted in spin dynamics calculations of a donor–acceptor electron spin pair P + A 1A - created upon light excitation of photosystem I (PSI). The effects of nuclear spin diffusion (NSD) due to the anisotropic protein environment on the predicted coherence times T M are studied. Closely positioned pairs and triples of protons located 5–8 Å from the electron spin are shown to largely control T M . Additionally, knowledge of the crystal structure of PSI allows for spin dynamics calculations where specific cofactors and amino acid residues are removed or replaced and the identification of anisotropic environmental features controlling electronic decoherence. Finally, we show that NSD alone cannot explain the >3× shorter experimentally observed coherence times and suggest that methyl groups at key protein sites may explain this discrepancy.

74 ATOMIC AND MOLECULAR PHYSICS↗