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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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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A biosynthetic gene cluster for three post-chorismate pathways in Arabidopsis

Chorismate is a branch-point metabolite in the biosynthesis of aromatic amino acids, vitamins, antibiotics and various other aromatic products in bacteria, fungi and plants. Although 13 chorismate-utilizing enzymes have been identified in bacteria, only 6 have been described in plants, where an estimated 30% of all photosynthetically fixed carbon passes through chorismate. Here, in this study, we describe a biosynthetic gene cluster (BGC) consisting of five core genes, including two reductases, two methyltransferases and one glucosyltransferase. Genetic and biochemical evidence shows that these five enzymes collectively give rise to three biosynthetic pathways, each originating from chorismate: two parallel pathways produce a class of non-aromatic, isomeric compounds abundant in the roots of Arabidopsis thaliana, whereas the third pathway produces methylated and glucosylated chorismate derivatives that subsequently react non-enzymatically with glutathione. Genome analysis revealed that variants of this BGC are present in some but not all species in the Brassicaceae family. Taken together, our study uncovered a BGC, containing three chorismate-utilizing enzymes, that controls three distinct post-chorismate pathways in A. thaliana. This work not only advances our understanding of carbon flow in this model plant but also highlights that the biochemical complexity encoded by plant BGCs is greater than previously appreciated.

Peng, Meng [Ghent Univ. (Belgium); Flemish Institu↗
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 OSTI2024

Bragg glass signatures in Pd x ErTe 3 with X-ray diffraction temperature clustering

The Bragg glass phase is a nearly perfect crystal with glassy features predicted to occur in vortex lattices and charge-density-wave systems in the presence of disorder. Detecting it has been challenging, despite its sharp theoretical definition in terms of diverging correlation lengths. Here we present bulk probe evidence supporting a Bragg glass phase in the systematically disordered charge-density-wave material of Pd x ErTe 3 . We do this by using comprehensive X-ray data and a machine-learning-based analysis tool called X-ray diffraction temperature clustering (X-TEC). We establish a diverging correlation length in samples with moderate intercalation over a wide temperature range. To enable this analysis, we introduced a high-throughput measure of inverse correlation length that we call peak spread. The detection of Bragg glass order and the resulting phase diagram advance our understanding of the complex interplay between disorder and fluctuations. Moreover, the use of our analysis technique to target fluctuations through a high-throughput measure of peak spread can revolutionize the study of fluctuations in scattering experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fast mass spectrometry search and clustering of untargeted metabolomics data

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Global characterization of biosynthetic gene clusters in non-model eukaryotes using domain architectures

Abstract The majority of pharmaceuticals are derived from natural products, bioactive compounds naturally synthesized by organisms to provide evolutionary advantages. Although the rich evolutionary history of eukaryotic algal species implicates a high potential for natural product-based drug discovery, it remains largely untouched. This study investigates 2762 putative biosynthetic gene clusters (BGCs) from 212 eukaryotic algal genomes. To analyze a vast set of structurally diverse BGCs, we employed comparative analysis based on the vectorization of biosynthetic domains, referred to as biosynthetic domain architecture (BDA). By characterizing core biosynthetic machineries through BDA, we identified key BDAs of modular BGCs in diverse eukaryotes and introduced 16 candidate modular BGCs with similar BDAs to previously validated BGCs. This study provides a global characterization of eukaryotic algal BGCs, offering an alternative to laborious manual curation for BGC prioritization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Spiral spin cluster in the hyperkagome antiferromagnet Mn 3 RhSi

Local spin correlation orders emerge in a paramagnetic state, with notable examples such as the partial order, cooperative paramagnetism, and soliton spin liquid. The noncentrosymmetric intermetallic antiferromagnet Mn 3 RhSi also exhibits the local spin correlation order in the paramagnetic state as magnetic short-range order in a wide temperature range. Here, we show that the local spin correlation order has a spiral structure by neutron scattering measurement of a Mn 3 RhSi single crystal. The possible origins of the magnetic cluster formation are discussed in terms of the Lifshitz invariant and the Griffiths phase, and compared with the room-temperature skyrmion phase of Co 7 Zn 7 Mn 6 and non-Fermi liquid behavior of β-Mn.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Benchmark relativistic delta-coupled-cluster calculations of K-edge core-ionization energies of third-row elements

Here a benchmark computational study of K-edge core-ionization energies of third-row elements using relativistic delta-coupled-cluster (ΔCC) methods and a revised core-valence separation (CVS) scheme is reported. High-level relativistic (HLR) corrections beyond the spin-free exact two-component theory in its one-electron variant (SFX2C-1e), including the contributions from two-electron picture-change effects, spin–orbit coupling, the Breit term, and quantum electrodynamics effects, have been taken into account and demonstrated to play an important role. Relativistic ΔCC calculations are shown to provide accurate results for core-ionization energies of third-row elements. The SFX2C-1e-CVS-ΔCC results augmented with HLR corrections show a maximum deviation of less than 0.5 eV with respect to experimental values.

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

Higher loadings of Pt single atoms and clusters over reducible metal oxides: application to C–O bond activation

We develop higher loadings of isolated noble metal atoms and clusters on a mildly reducible metal oxide. Here we demonstrate the approach for Pt supported on TiO 2 and confirmed it by XRD, AC-HAADF-STEM, CO-FTIR, XAS, and XPS. Density functional theory calculations rationalize the experimental stability and the IR shifts using mixtures of CH 3 I and CO. The redispersed catalysts are thermally stable in inert gas or H 2 and afford enhanced selectivity and activity in hydrodeoxygenation reactions compared to metal nanoparticles by creating surface oxygen vacancies that promote C–O cleavage without side reactions. Higher metal loadings, e.g., 1%Pt/TiO 2 , on the oxide surface profoundly increase the activity of the bare oxide catalyst tenfold compared to ultra-low loadings typically used for single atom catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H–L , and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H–L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol –1 MAE) for robust VHTS.

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

ThC 2 @C 82 versus Th@C 84 : unexpected formation of triangular thorium carbide cluster inside fullerenes

Synthesis of the first thorium-containing clusterfullerenes, ThC 2 @C s (6)–C 82 and ThC 2 @C 2 (5)–C 82 , is reported. These two novel actinide fullerene compounds were characterized by mass spectrometry, single-crystal X-ray diffraction crystallography, UV–vis–NIR spectroscopy, and theoretical calculations. Crystallographic studies reveal that the encapsulated ThC 2 clusters in both C s (6)–C 82 and C 2 (5)–C 82 feature a novel bonding structure with one thorium metal center connected by a C≡C unit, forming an isosceles triangular configuration, which has not been hitherto observed for endohedral fullerenes or for solid phase thorium carbides. Electronic structure calculations assign a formal electronic structure of [Th 4+ (C 2 ) 2- ] 2+ @[C 82 ] 2- , with pronounced donation bonding from (C 2 ) 2- to Th 4+ , secondary backbonding from the fullerene to thorium and Th–C double bond character in both compounds. This work presents a new family of endohedral fullerenes, MC 2 @C 2n-2 , being unexpected isomers of MC 2n , and provides broader understanding of thorium bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗