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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 235 records · Page 13

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery

Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Lastly, we demonstrate that the cis model may be combined with Δ-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Generating Protein Structures for Pathway Discovery Using Deep Learning

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stabilization of Preternatural Barium Oxidation States as an Unexpected Byproduct of β-Decay: Discovery of a New Halide Semiconductor Alloy

137 Cs has a wide range of roles in the nuclear industry. The solid material, safely encapsulated in CsCl as 137 CsCl, is stored as fission product waste from nuclear power production and legacy waste from nuclear weapons production; it has also served as a radiation source in food and sewage irradiators as well as medical devices. However, because of the solubility of the chloride salt and the relatively high specific activity of 137 Cs, damaged or broken capsules can lead to severe radiological accidents. Safe capsule design and material recycling are complicated by the unclear structural evolution during β-decay, which remains ambiguous due to the differing oxidation states of Cs (1+) and Ba (2+). Here, in this study, we use first-principles calculations to investigate the evolving structure–property relationships of Cs 1–x Ba x Cl during β-decay. Despite the well-established 2+ formal oxidation state of alkali-earth metals, we find that Ba 1+ can be stabilized in the form of a mixed-valence alloy at low concentrations. Specifically, we identify three regimes for the β-decay of 137 Cs into CsCl: Ba-doped CsCl (Ba ≤ 14%), wherein Ba has the expected 2+ oxidation state; Cs–Ba–Cl alloys, where Ba has a mix of the usual Ba 2+ and highly unusual Ba 1+ oxidation state in the form of a quasi-disordered mixed-valence alloy (Ba = 25%); and phase separation into a CsCl + BaCl 2 + Ba (m) mechanical mixture, where Ba reverts to its expected 2+ oxidation state (Ba > 25%). Surprisingly, the Cs 0.75 Ba 0.25 Cl mixed-valence alloy is a narrow indirect band gap semiconductor (1.05 eV) despite the insulating nature of both CsCl and BaCl 2 . It also exhibits strongly excitonic polarized optical properties, has glass-like ultralow thermal conductivity (directional average of 0.21 W/mK at 300 K), and shows greater resistance to deformation under both tensile and volumetric strengths compared with the original CsCl structure (e.g., shear and Young’s modulus of 9.04 and 31.62 GPa, respectively). These findings imply that transmutation of 137 Cs leads to highly unusual chemical bonding that stabilizes Ba 1+ in local regions of the quasi-disordered Cs 0.25 Ba 0.75 Cl, resulting in anomalous physical properties. Moreover, this discovery provides valuable insight for safe nuclear waste capsule design, which can aid in preventing environmental or human exposure to radioactive materials.

Fuhr, Addis S. [Oak Ridge National Laboratory (ORN↗

Discovery of a Stripe Phase in an Elemental Solid

Translational symmetry breaking is foundational to condensed matter physics because it is associated with crystal formation. At much lower energy scales, the breaking of crystalline translational symmetry can be driven by electronic, rather than ionic, degrees of freedom and may give rise to stripe order, a unidirectional ordered state. Such symmetry breaking has been seen in two-dimensional and strongly correlated systems. Here, for the first time, we report the observation of stripe order in an elemental solid, tellurium. Through topographic and spectroscopic imaging, we discover a commensurate 4 × 1 stripe phase. Surprisingly, this exotic order is so robust that it survives close to room temperature. Notably, our diffraction experiments confirm the bulk nature of the stripe order, showing the minuteness of potential lattice distortion associated with the order. In conclusion, our discovery of the stripe order in tellurium opens new windows to understanding the spontaneous symmetry breaking in elemental solids.

charge order↗

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↗

Reaction Discovery Using Spectroscopic Insights from an Enzymatic C–H Amination Intermediate

Engineered hemoproteins can selectively incorporate nitrogen from nitrene precursors like hydroxylamine, O-substituted hydroxylamines, and organic azides into organic molecules. Although iron-nitrenoids are often invoked as the reactive intermediates in these reactions, their innate reactivity and transient nature have made their characterization challenging. Here we characterize an iron-nitrosyl intermediate generated from NH 2 OH within a protoglobin active site that can undergo nitrogen-group transfer catalysis, using UV–vis, electron paramagnetic resonance (EPR) spectroscopy, and high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) techniques. The mechanistic insights gained led to the discovery of aminating reagents—nitrite (NO 2 – ), nitric oxide (NO), and nitroxyl (HNO)—that are new to both nature and synthetic chemistry. Based on the findings, we propose a catalytic cycle for C–H amination inspired by the nitrite reductase pathway. Furthermore, this study highlights the potential of engineered hemoproteins to access natural nitrogen sources for sustainable chemical synthesis and offers a new perspective on the use of biological nitrogen cycle intermediates in biocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Mechanism-Based Reaction–Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis

Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction–diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility toward accelerated discovery of new resin formulations. In this work, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100 °C, remains valid under the nonideal conditions encountered in FROMP, including ambient to >200 °C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts the FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., T max ). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model’s potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Discovery of an Interlocked and Interwoven Molecular Topology in Nanocarbons via Dynamic C–C Bond Formation

Topologically complex carbon nanostructures are an exciting but largely unexplored class of materials due to their challenging synthesis. Previous methods are low yielding because they rely on irreversible C sp 2 −C sp 2 bond formation, which necessitates complex templating strategies to enforce entanglement. Here, reversible zirconocene coupling of alkynes is developed as a new method to access complex molecular topologies, where dynamic C−C bond formation facilitates entanglement under thermodynamic control, allowing the use of simple precursors without the need for preassembly. This strategy enables the scalable, high-yield synthesis of three topologically distinct nanocarbons, including the serendipitous discovery of a structure containing a new topological motif that was not previously identified or realized synthetically. This motif, consisting of an unusual combination of interlocking and interweaving, was recognized to be generalizable to a new topological class of molecules, introduced here as perplexanes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Discovery and Synthesis of a Family of Boride Altermagnets

Borides are a rich material family. To push the boundaries of borides’ properties and applications into broader fields, we have conducted systematic theoretical and experimental searches for synthesizable phases in ternary borides TM 2 B 2 (T = 3d, M = 4d/5d transition metals). We find that TM 2 B 2 in the FeMo 2 B 2 -type and CoW 2 B 2 -type structures form a large family of stable/metastable materials of 120 members. Among them, we identify 40 materials with stable magnetic solutions. Further, we discover 11 altermagnets in the FeMo 2 B 2 -type structure. So far, boride altermagnets are rare. In these altermagnets, T = Fe or Mn atoms are arranged in parallel T-chains with strong ferromagnetic intrachain couplings and antiferromagnetic interchain couplings. They simultaneously exhibit electronic band spin splitting, typical of ferromagnetism, and zero net magnetization, typical of antiferromagnetism. They also exhibit magnonic band chiral splitting. Both effects originate from the unique altermagnetic symmetries crucially constrained by the nonmagnetic atoms in the structure. Transport properties of relevance to spintronic applications, including the strain-induced spin-splitter effect and anomalous Hall effect, are predicted. An iodine-assisted synthesis method for TM 2 B 2 is developed, using which 7 of the predicted low-energy phases are experimentally synthesized and characterized, including 4 altermagnets. This work expands the realm of borides by offering new opportunities for studying altermagnetism and altermagnons in borides. It also provides valuable insights into the discovery and design of altermagnets. Here, by demonstrating that altermagnets can exist as families sharing a common motif, this work paves a feasible route for discovering altermagnets by elemental substitutions and high-throughput computations.

Chemical structure↗

High-Throughput Discovery Illuminates Design Principles and Limits for Long-Lived Charged Species in Organic Electrolytes

The chemical stability of charged molecules in all-organic redox flow batteries (RFBs) is required for the prolonged operation of these devices. Molecular engineering and electrolyte optimization are used to mitigate parasitic reactions and extend the lifetimes of the charge carriers. However, how much can structural variation extend the lifetime? To probe this query, we designed a high-throughput kinetic study of the radical cation of N-methylphenothiazinium, guided by statistical sampling and learning algorithms. Using Argonne’s autonomous discovery facility, we conducted over 6,000 kinetic experiments with robotic sample preparation, parallel kinetic measurements, and machine learning inputs, testing 188 solvent molecules selected from a space of over 540 candidates from 11 chemical classes. Algorithmic selections guided us to stable solvent candidates, which were further tested in high concentration with and without supporting electrolyte. Our findings reveal the inherent difficulty of exceeding the current state of the art through solvent variation. The desired stability is statistically rare and poorly predictable. Among the many tested, only three solvents significantly outperformed our baseline, acetonitrile─and none by more than a factor of 3─suggesting a general challenge in achieving the necessary techno-economic targets. Furthermore, we suggest that self-discharge through solvent homolysis is the cause of the observed limitations. Several structural motifs contribute to >1,000 h half-life stability including molecular simplicity, symmetry, oxidation complement, and strategic fluorination. Importantly, this workflow establishes effective assays for diagnosing and predicting oxidative stress for highly stable liquid electrolytes in all batteries.

Batteries↗

Scenario Storyline Discovery for Planning in Multi‐Actor Human‐Natural Systems Confronting Change

Scenarios have emerged as valuable tools in managing complex human-natural systems, but the traditional approach of limiting focus on a small number of predetermined scenarios can inadvertently miss consequential dynamics, extremes, and diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by exploring a wide range of possible futures and identifying those that yield consequential vulnerabilities. However, vulnerabilities are typically identified based on aggregate robustness measures that do not take full advantage of the richness of the underlying dynamics in the large ensembles of model simulations and can make it hard to identify key dynamics and/or storylines that can guide planning or further analyses. This study introduces the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced “forensic”): a scenario discovery framework that addresses these challenges by organizing and investigating consequential scenarios using hierarchical classification of diverse outcomes across actors, sectors, and scales, while also aiding in the selection of scenario storylines, based on system dynamics that drive consequential outcomes. We present an application of this framework to the Upper Colorado River Basin, focusing on decadal droughts and their water scarcity implications for the basin's diverse users and its obligations to downstream states through Lake Powell. We show how FRNSIC can explore alternative sets of impact metrics and drought dynamics and use them to identify drought scenario storylines, that can be used to inform future adaptation planning.

54 ENVIRONMENTAL SCIENCES↗

Discovery of van Hove singularities: electronic fingerprints of 3Q magnetic order in a van der Waals quantum magnet

Magnetically intercalated transition metal dichalcogenides are emerging as a rich platform for exploring exotic quantum states in van der Waals magnets. Among them, Co x TaS 2 has attracted intense interest following the recent discovery of a distinctive 3Q magnetic ground state and a pronounced topological Hall effect below a critical doping of x ≈ 1/3, both intimately tied to cobalt concentration. To date, direct signatures of this enigmatic 3Q magnetic order in the electronic structure remain elusive. Here we report a comprehensive doping dependent angle resolved photoemission spectroscopy study that unveils these long-sought fingerprints. Our data reveal an unexpected inverse-Mexican-hat dispersion along the K-M-K' direction, accompanied by two van Hove singularities. These features are consistent with theoretical predictions for a 3Q magnetic order near three-quarters band filling on a cobalt triangular lattice. These results provide evidence of 3Q magnetic order in the electronic structure, establishing TMD van der Waals magnets as tunable materials to explore the interplay between magnetism and topology.

Luo, Hai-Lan [University of California, Berkeley, ↗

Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage

The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.

Li, He↗

Microbial secondary metabolites: advancements to accelerate discovery towards application

Microbial secondary metabolites not only have key roles in microbial processes and relationships but are also valued in various sectors of today’s economy, especially in human health and agriculture. The advent of genome sequencing has revealed a previously untapped reservoir of biosynthetic capacity for secondary metabolites indicating that there are new biochemistries, roles and applications of these molecules to be discovered. New predictive tools for biosynthetic gene clusters (BGCs) and their associated pathways have provided insights into this new diversity. Advanced molecular and synthetic biology tools and workflows including cell-based and cell-free expression facilitate the study of previously uncharacterized BGCs, accelerating the discovery of new metabolites and broadening our understanding of biosynthetic enzymology and the regulation of BGCs. These are complemented by new developments in metabolite detection and identification technologies, all of which are important for unlocking new chemistries that are encoded by BGCs. This renaissance of secondary metabolite research and development is catalysing toolbox development to power the bioeconomy.

Dinglasan, Jaime Lorenzo N↗

Discovery of FoTO1 and Taxol genes enables biosynthesis of baccatin III

Abstract Plants make complex and potent therapeutic molecules 1,2 , but sourcing these molecules from natural producers or through chemical synthesis is difficult, which limits their use in the clinic. A prominent example is the anti-cancer therapeutic paclitaxel (sold under the brand name Taxol), which is derived from yew trees (Taxusspecies) 3 . Identifying the full paclitaxel biosynthetic pathway would enable heterologous production of the drug, but this has yet to be achieved despite half a century of research 4 . WithinTaxus’ large, enzyme-rich genome 5 , we suspected that the paclitaxel pathway would be difficult to resolve using conventional RNA-sequencing and co-expression analyses. Here, to improve the resolution of transcriptional analysis for pathway identification, we developed a strategy we term multiplexed perturbation × single nuclei (mpXsn) to transcriptionally profile cell states spanning tissues, cell types, developmental stages and elicitation conditions. Our data show that paclitaxel biosynthetic genes segregate into distinct expression modules that suggest consecutive subpathways. These modules resolved seven new genes, allowing a de novo 17-gene biosynthesis and isolation of baccatin III, the industrial precursor to Taxol, inNicotiana benthamianaleaves, at levels comparable with the natural abundance inTaxusneedles. Notably, we found that a nuclear transport factor 2 (NTF2)-like protein, FoTO1, is crucial for promoting the formation of the desired product during the first oxidation, resolving a long-standing bottleneck in paclitaxel pathway reconstitution. Together with a new β-phenylalanine-CoA ligase, the eight genes discovered here enable the de novo biosynthesis of 3’-N-debenzoyl-2’-deoxypaclitaxel. More broadly, we establish a generalizable approach to efficiently scale the power of co-expression analysis to match the complexity of large, uncharacterized genomes, facilitating the discovery of high-value gene sets.

Science & Technology - Other Topics↗

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗