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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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General review of code rules What do they cover. What they don’t. Underlying assumptions Probability of failure Material property parameters Stresses and loads Degradation What is NonmetallicsWork Group (NWG) doing? Failure in components Redefining failure Degradation rules Oxidation degradation Irradiation degradation Molten Salt Issues Abrasion/Erosion
Small-molecule organic semiconductors are promising materials for applications ranging from solar cells to medical sensors. Their nearly infinite design space means that, in theory, it is possible to tailor the material to the exact specifications of a particular application. In reality; however, design rules to improve mobility (μ) remain elusive because of the complex calculations required to understand its limiters. Transient localization theory posits that charge carriers are slowed down by the collective phonon motions, called dynamic disorder, which localize charge carriers temporarily. Traditionally, a mode-by-mode analysis is performed to try and identify “killer” modes. Herein, the flaws are demonstrated with this analysis and present a streamlined simulation workflow that simplifies simulation of dynamic disorder and enables engineering of new molecular structures based on phonon dynamics. This workflow is combined with a novel visualization technique that enables per-atom insights into how μ is limited. Furthermore, this workflow is applied and analyzed to a series of -acenes and a series of BTBT-based molecules. Then design rules are identified in each series that identify possible ways to improve the μ beyond current experimental limits using phonon engineering.
Products are often discarded when they fail or no longer meet user needs. These outcomes are frequently shaped by early design decisions. While remanufacturing offers a sustainable alternative by restoring products to like‐new condition, its potential is often limited by designs that do not consider remanufacturing from the outset. This research addresses that challenge by introducing a structured Design for Remanufacturing (DfRem) methodology and a CAD‐integrated tool to support real‐time design decisions. The DfRem framework introduces a new primary design function focused on preserving product functionality across its life cycle. It is supported by a fault tree that identifies failure modes that limit remanufacturing potential and a hierarchy of design principles including Prevent, Minimize, Relocate, Restore, and others. Each principle is linked to actionable design rules that help engineers reduce the need for remanufacturing or improve its efficiency when necessary. To operationalize this framework, we developed CAD plugins for Autodesk Inventor and PTC Creo. These tools use a state machine model to present prioritized design rules based on selected failure modes and user input. By embedding DfRem logic directly into widely used CAD environments, the tool enables engineers to make sustainability‐informed decisions without disrupting existing workflows. Furthermore, this approach highlights the critical role of design in enabling circular and resource‐efficient product development, making remanufacturing a more practical and accessible strategy during the early stages of product design.
Tailoring molecular structures of organic electrode materials (OEMs) can enhance their performance in Na-ion batteries, however, the substitution rules and the consequent effect on the specific capacity and working potential remain elusive. Herein, by examining three sodium carboxylates with selective N substitution or extended conjugation structure, we exploited the correlation between structure and performance to establish substitution rules for high-capacity OEMs. Our results show that substitution position and types of functional groups are essential to create active centers for uptake/removal of Na+ and thermodynamically stabilize organic structures. Furthermore, rational host design and electrolytes modulation were performed to extend the cycle life to 500 cycles. A full cell based on the optimal 2,2’-bipyridine-4,4’-dicarboxylic acid disodium salt anode and the polyaniline cathode is demonstrated to confirm the feasibility of achieving all-organic batteries. Lastly, this work provides a valuable guideline for the design principle of high-capacity and stable OEMs for sustainable energy storage.
Understanding activation barriers controlling structural relaxation in glass-forming liquids, molecular transport, and ionic conductivity in amorphous polymers is a grand challenge of fundamental scientific and materials engineering interest across disciplines. Over decades, intriguing but puzzling empirical correlations between the elementary time scale of activated barrier crossing and the apparent Arrhenius activation energy, the so-called Meyer−Neldel (MN) rules, have been discovered in diverse liquids and glasses. Here, in this study, we formulate and successfully apply a new experimental analysis and an explicitly dynamical theoretical framework which provides an understanding of the origin, validity, and failure of such correlations, that bridge and unify the three fields of structural relaxation, molecular transport, and ionic conductivity in liquids and quenched glasses. Distinct quasi-universal laws are predicted in equilibrated liquids and nonequilibrium glasses, consistent with experiments. Our analysis reveals that even if the relaxation appears Arrhenius over a limited temperature range, the physical activation barrier is generally temperature-dependent in polymeric systems even below glass transition temperature. In addition, we show that the approximate validity of classical MN rules hinges on a linear temperature dependence of this barrier and the temperature range probed in experiments. Our findings are relevant for controlling the activation barrier in functional soft polymeric materials relevant to molecular separations, barrier coatings, and charge transport, and also provide new constraints on the theoretical understanding of the mechanism underlying slow activated dynamics in glass-forming condensed matter.
ASME Section III, Division 5 B Rules and supporting slides for code classes, construction rules, gaps in Methodology, Primary Load & Strain Limits, short and long dwell time, and future work and development including ongoing evaluations.
Abstract Zavitsas developed an aqueous electrolyte thermodynamic model and applied it to single electrolyte solutions previously. Here, a method to apply the model to multicomponent solutions is derived by setting the water activity to the mole fraction of free water in the mixture. The method is shown to work effectively for the CsBr‐KNO 3 ‐H 2 O and CsCl‐KNO 3 ‐H 2 O systems at 100.3°C even at concentrations greater than 6 molal. The largest error in the prediction of water activities was just −0.00365 for the CsBr‐KNO 3 ‐H 2 O system and −0.00631 for the CsCl‐KNO 3 ‐H 2 O system. Zavitsas' model naturally obeys Zdanovskii's rule because when two electrolyte solutions with the same fraction of free water are mixed together, water is obviously going to have that same fraction in the mixture. This means that Zavitsas' model will likely work well for the many electrolytes that obey Zdanovskii's rule.
We investigate the relationship between superselection rules and quantum error correcting codes. We demonstrate that the existence of a superselection rule implies the Knill-Laflamme condition in quantum error correction. As an example, we examine the code built from quantum chromodynamics, where the proton and neutron states in the model are explored as different superselection sectors that protect logical information. Finally we comment on topological quantum error correcting codes and supersymmetric quantum field theory within this framework.
The first isomerization reaction of an alkylperoxy (RO 2 ) radical holds significant importance in low-temperature oxidation, as it governs the branching ratios of the hydroperoxyalkyl (QOOH) radicals, which influence the competition between the chain-propagation and chain-branching reactions. In this study, we systematically calculated high-pressure rate rules for the RO 2 isomerization reaction of monoethers, exploring 5-, 6-, 7-, and 8-membered ring transition states. Primary, secondary, and tertiary carbon sites, where both the abstracting peroxy group and the abstracted hydrogen are located, were considered, with particular emphasis on distinguishing between secondary carbons adjacent (alpha) and nonadjacent to the ether functional group. Using the G4//B3LYP/6-311++G(2df,2pd) level of theory and the transition state theory, we estimated the rate constants and the Arrhenius coefficient for over 120 possible isomerization reactions. We examined the effect of ring size and ring atoms, revealing that 6- and 7-membered ring isomerizations were generally the fastest. The impact of the ether functional group on transition states was investigated by comparing reactions with identical ring size, peroxy, and radical positions, but with the ether functional group positioned either outside (i.e., out) or inside (i.e., in) the transition state ring, leading to differences in the rate constants. When comparing to analogous alkane rate constants, differences of up to an order of magnitude were observed, underscoring the need for caution when assigning rate rules by analogy. We applied our rate constants in the di-iso-butyl ether kinetic model and evaluated their influence on low-temperature chemistry finding that they altered the branching ratios by up to a factor of 9, highlighting the significance of site-specific rate constants for more accurate low-temperature modeling.
Open-shell π-conjugated systems have emerged as promising molecular wires that can sustain unusually high low-bias conductance over tens of nanometers. However, predictive design rules remain limited because near-degeneracy and spin polarization give rise to correlated electronic states that are not reliably captured by standard density functional theory and Landauer transport descriptions. Here, we establish physically transparent design rules for long-range, weakly length-dependent charge transport based on the interplay between bond-length alternation and electron–electron interactions. Using a fully correlated transport framework that combines density matrix renormalization group calculations with nonequilibrium Green’s function embedding, we show that weak dimerization maintains spatially extended edge states, while moderate on-site interaction stabilizes open-shell character without excessively separating transport-relevant resonances. This balance yields zero-bias transmission that is largely insensitive to molecular length, providing a route to connect chemically tunable structure to correlated transport beyond mean-field descriptions.
In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure.
Abstract While geometrically frustrated quantum magnets host rich exotic spin states with potentials for revolutionary quantum technologies, most of them are necessarily good insulators which are difficult to be integrated with modern electrical circuit. The grand challenge is to electrically detect the emergent fluctuations and excitations by introducing charge carriers that interact with the localized spins without destroying their collective spin states. Here, we show that, by designing a Bi 2 Ir 2 O 7 /Dy 2 Ti 2 O 7 heterostructure, the breaking of the spin-ice rule in insulating Dy 2 Ti 2 O 7 leads to a charge response in the conducting Bi 2 Ir 2 O 7 measured as anomalous magnetoresistance during the field-induced Kagome ice-to-saturated ice transition. The magnetoresistive anomaly also captures the characteristic angular and temperature dependence of this ice-rule-breaking transition, which has been understood as magnetic monopole condensation. These results demonstrate a novel heteroepitaxial approach for electronically probing the transition between exotic insulating spin states, laying out a blueprint for the metallization of frustrated quantum magnets.
Abstract In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.
Density functional theory (DFT) and its extensions, such as DFT+U and DFT+dynamical mean-field theory, are invaluable for studying magnetic properties in solids. However, rare-earth (R) materials remain challenging due to self-interaction errors and the lack of proper orbital polarization. We show how the orbital dependence of self-interaction error contradicts Hund’s rules and plagues magnetocrystalline anisotropy (MA) calculations, and how analyzing DFT states that respect Hund’s rules can mitigate this issue. We benchmark MA in RCo 5 , R 2 Fe 14 B, and RFe 12 , extending prior work on RMn 6 Sn 6 , achieving excellent agreement with experiments. Additionally, we illustrate a semi-analytical perturbation approach that treats crystal fields as a perturbation in the large spin-orbit coupling limit. Using Gd-4f crystal-field splitting, this method provides a microscopic understanding of MA and enables rapid screening of high-MA materials.
Learning to integrate non-linear equations from highly resolved direct numerical simulations has seen recent interest for reducing the computational load for fluid simulations. Here, we focus on determining a flux-limiter for shock capturing methods. Focusing on flux limiters provides a specific plug-and-play component for existing numerical methods. Since their introduction, an array of flux limiters has been designed. Using the coarse-grained Burgers' equation, we show that flux-limiters may be rank-ordered in terms of their log-error relative to high-resolution data. We then develop a theory to find an optimal flux-limiter and present flux-limiters that outperform others tested for integrating Burgers' equation on lattices with [Formula: see text], and 2x, 3x, 4x, and 8x coarse-grainings. We train a continuous piecewise linear limiter by minimizing the mean-squared misfit to six-grid point segments of high-resolution data, averaged over all segments. While flux limiters are generally designed to have an output of φ(r) = 1 at a flux ratio of r = 1, our limiters are not bound by this rule and yet produce a smaller error than standard limiters. Here we find that our machine learned limiters have distinctive features that may provide new rules-of-thumb for the development of improved limiters. Additionally, we use our theory to learn flux-limiters that outperform standard limiters across a range of values (as opposed to at a specific fixed value) of coarse-graining, number of discretized bins, and diffusion parameter. This demonstrates the ability to produce flux limiters that should be more broadly useful than standard limiters for general applications.
A unified theory is presented for finite-temperature many-body perturbation expansions of the anharmonic vibrational contributions to thermodynamic functions, i.e., the free energy, internal energy, and entropy. The theory is diagrammatically size-consistent at any order, as ensured by the linked-diagram theorem proved in this study, and, thus, applicable to molecular gases and solids on an equal footing. It is also a basis-set-free formalism, just like its underlying Bose–Einstein theory, capable of summing anharmonic effects over an infinite number of states analytically. It is formulated by the Rayleigh–Schrödinger-style recursions, generating sum-over-states formulas for the perturbation series, which unambiguously converges at the finite-temperature vibrational full-configuration-interaction limits. Two strategies are introduced to reduce these sum-over-states formulas into compact sum-over-modes analytical formulas. One is a purely algebraic method that factorizes each many-mode thermal average into a product of one-mode thermal averages, which are then evaluated by the thermal Born–Huang rules. Canonical forms of these rules are proposed, dramatically expediting the reduction process. The other is finite-temperature normal-ordered second quantization, which is fully developed in this study, including a proof of thermal Wick’s theorem and the derivation of a normal-ordered vibrational Hamiltonian at finite temperature. The latter naturally defines a finite-temperature extension of size-extensive vibrational self-consistent field theory. These reduced formulas can be represented graphically as Feynman diagrams with resolvent lines, which include anomalous and renormalization diagrams. Two order-by-order and one general-order algorithms of computing these perturbation corrections are implemented and applied up to the eighth order. The results show no signs of Kohn–Luttinger-type nonconvergence.
Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.
Nuclear-spin conversion in molecular hydrogen is governed by strict symmetry rules that typically require magnetic fields or catalytic surfaces to break. Here we demonstrate that the intrinsic tensor composition of a non-magnetic molecular crystal field can impose and relax these rules without external fields. High-resolution infrared spectra of H 2 in crystalline CO 2 reveal large rank-2 (quadrupolar) crystal-field splittings of the 𝑚 sublevels, while nuclear-spin conversion occurs only through 𝛥𝑚 = 0 channels. Replacing CO 2 with polar N 2 O introduces rank-1 (dipole) components that partially open 𝛥𝑚 ≠ 0 pathways, while incorporation of paramagnetic NO 2 fully lifts the restriction. Furthermore, these results establish a direct correspondence between crystal-field tensor rank and nuclear-spin dynamics, introducing a general symmetry-based framework for designing and controlling spin-isomer populations and quantum-state connectivity in molecular solids.