Surface-to-space acoustic propagation model validation using chemical explosion sources: The DARPA AIRWaveS Project
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Nayak, Michael↗
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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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Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria
Drug discovery faces a crisis. The industry has used up the “obvious” space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. Here, we present proof of concept of an approach to rationally identify a “chemical vocabulary” related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Uncertainty-driven dynamics for active learning of interatomic potentials
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions.
97 MATHEMATICS AND COMPUTING↗
High throughput exploration of the oxidation landscape in high entropy alloys
We report high entropy alloys (HEAs) have gained interest for structural applications in extreme environments. With a potentially vast chemical and phase space, there are significant opportunities to discover superior performing alloys. Crucial for most high-temperature applications is understanding and mitigating the oxidation behavior of these chemically complex alloys. Most experimental and computational HEA studies have focused on a limited set of compositions and only a fraction these compositions have been characterized for oxidation. We present a high-throughput framework that utilizes density-functional theory (DFT) in concert with a combined machine-learning model and grand-canonical linear programming for assessing phase stability, phase-fraction, chemical activity and high-temperature survivability of arbitrary HEAs. This framework considers temperature dependent contributions to the Gibbs energy of the competing phases arising from short-range order and vibrational entropy. We demonstrate the effectiveness of the framework by assessing the thermodynamic stability, oxidation behavior, chemical activity, and phase decomposition of body-centered cubic Mo-W-Ta-Ti-Zr refractory HEAs. A total of 51 compositions were analyzed and ranked in order of their survivability based on the Pareto-front analysis. Oxidation was performed at 1373 K on fours samples in air showing the difference in oxidation behavior determined experimentally through scale thickness and their mass changes. The insights on oxidation behavior presented in this work will enable the fast assessment of technologically useful HEAs needed for future structural application in extreme conditions
36 MATERIALS SCIENCE↗
In silico design of microporous polymers for chemical separations and storage
Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. In this work, we highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.
adsorption↗
Influence of point defects on the electronic and topological properties of monolayer WTe 2
In some topological insulators such as monolayers of WTe 2 or graphene with spin-orbit coupling, band inversion originates from chemical bonding and space group symmetry, in contrast to materials such as Bi 2 Se 3 , where the band inversion derives from relativistic effects in the atoms. In the former, band inversion is susceptible to changes of the chemical environment, e.g., by defects, while the latter are less affected by defects due to the larger energy scale associated with atomic relativistic effects. Motivated by recent experiments, we study the effect of Te vacancies and Te adatoms on the electronic properties of WTe 2 . We find that the Te vacancies have a formation energy of 2.21 eV, while the formation energy of the Te adatoms is much lower with 0.72 eV. Here, the vacancies strongly influence the band structure and we present evidence that band inversion is already reversed at the nominal composition of WTe 1.97 . In contrast, we show that the adatoms do not change the electronic structure in the vicinity of the Fermi level and thus the topological properties.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Time and space resolved diagnostics for plasma thermal-chemical instability of fuel oxidation in nanosecond plasma discharges
Here, an instability in a nanosecond pulsed dielectric barrier discharge plasma occurring in methane–oxygen–argon mixtures is experimentally observed and measured by 1D time-resolved in situ electric field measurements. This instability, which seems to be created by the positive feedback between plasma kinetics and plasma-assisted low temperature fuel oxidation, is studied using electric field induced second harmonic generation and direct ICCD imaging. The rapid formation of streamers from an originally uniform discharge appears to be caused by the chemical kinetics of plasma-assisted low temperature methane oxidation, which may be resulting in a new type of plasma instability: a thermal-chemical instability. The results also revealed that the occurrence of this possible thermal-chemical instability in a reactive flow drastically changes the plasma properties by forming multiple secondary discharges and possibly leads to micron-sized non-uniform electric distributions. Single shot uncalibrated measurements of the electric field of the micron sized streamers appears to show much greater strengths than the average electric field. Furthermore, one-dimensional data analysis shows the positive feedback loop between the streamers and the low temperature plasma assisted oxidation chemistry in the plasma thermal-chemical instability. The present finding advances the understanding plasma instability growth and provides a new way to control plasma uniformity in plasma-assisted combustion and plasma fuel reforming.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Chemical vapor deposition of ZrC coatings for Space Nuclear Propulsion
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Lopez Honorato, Eddie↗
Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids
Polymer–protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer–protein hybrid materials.
36 MATERIALS SCIENCE↗
MISPR : an open-source package for high-throughput multiscale molecular simulations
Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.
36 MATERIALS SCIENCE↗
Unraveling the dislocation–precipitate interactions in high-entropy alloys
The precipitates play a significant role in not only enhancing the strength, but also maintaining the high toughness in alloys. However, the interactions of the nanoscale precipitates with dislocations in the high entropy alloys (HEAs) are difficult to observe directly by in-situ TEM experiments due to the limits of the resolution and time. Here, using atomic simulations we report the synergistic strengthening of the coherent precipitate and atomic-scale lattice distortion in the HEAs at cryogenic/elevated temperatures. The effects of temperature, chemical disorder, precipitate spacing, precipitate size, elemental segregation, and dislocation-cutting number on the critical stress for the dislocation to overcome a row of precipitates are studied. A random stacking fault energy landscape along the slip plane, the lattice distortion at different temperatures, and the interface/surface energy at various crystallographic orientations are obtained. Compared with the traditional metals and alloys, HEAs have the severe atomic-scale lattice distortions to generate the local high tensile/compressive stress fields. This complex stress causes the dislocation line to bend, and thus improves the dislocation slip resistance, resulting in the strong solid-solution strengthening. The stacking fault strengthening induced by the obvious difference of the stacking fault energies between the HEA matrix and precipitate (within the inner of the HEA matrix), and the formation of the antiphase domain boundary contribute to the high strength. The precipitate embedded by the solute atoms produces the strong lattice distortion to enhance the dislocation slip resistance at high temperatures. Hence, the current results provide the mechanistic insight into the phenomenon that the coherent precipitate combined with the severe atomic-scale lattice distortion can enhance the strength at cryogenic/elevated temperatures to further broaden the scope of applications of advanced HEAs.
36 MATERIALS SCIENCE↗
Accelerating computational modeling and design of high-entropy alloys
High-entropy alloys, with N elements and compositions {$c_{ν = 1,N}$} in competing crystal structures, have large design spaces for unique chemical and mechanical properties. In this work, to enable computational design, we use a metaheuristic hybrid Cuckoo search (CS) to construct alloy configurational models on the fly that have targeted atomic site and pair probabilities on arbitrary crystal lattices, given by supercell random approximates (SCRAPs) with S sites. Our Hybrid CS permits efficient global solutions for large, discrete combinatorial optimization that scale linearly in a number of parallel processors, and linearly in sites S for SCRAPs. For example, a four-element, 128-site SCRAP is found in seconds—a more than 13,000-fold reduction over current strategies. Our method thus enables computational alloy design that is currently impractical. We qualify the models and showcase application to real alloys with targeted atomic short-range order. Being problem-agnostic, our Hybrid CS offers potential applications in diverse fields.
36 MATERIALS SCIENCE↗
Accelerating catalytic advancements through the precision of high-throughput experiments & calculations
The growing demand for energy-efficient processes to support a sustainable future drives the need for research to rapidly explore chemical and material space through accelerated catalyst discovery initiatives. Recent breakthroughs in high-throughput experimental and computational methods are transforming the catalysis field, surpassing traditional approaches to manipulating variables in catalytic processes. Key advancements in innovation include the integration of machine learning for efficient catalyst screening, high-throughput experimentation, data-driven methodologies employing comprehensive databases, and in situ and in operando techniques for realistic observations. This progress has undoubtedly been intertwined with a collaborative framework across disciplines, reshaping catalyst discovery methods in both industry and academia. This Opinion article presents a multifaceted perspective from coauthors with expertise spanning various stages of the Technology Readiness Level spectrum, highlighting both opportunities and persistent challenges in integrating computational and experimental approaches in catalysis. These challenges span from obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions to navigating the complexity and predictive accuracy of computational models.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Characterization of switchgrass ( Panicum virgatum L.) PvKSL1 as a levopimaradiene/abietadiene‐type diterpene synthase
Abstract The diverse class of plant diterpenoid metabolites serves important functions in mediating growth, chemical defence, and ecological adaptation. In major monocot crops, such as maize (Zea mays), rice (Oryza sativa), and barley (Hordeum vulgare), diterpenoids function as core components of biotic and abiotic stress resilience. Switchgrass (Panicum virgatum) is a perennial grass valued as a stress‐resilient biofuel model crop. Previously we identified an unusually large diterpene synthase family that produces both common and species‐specific diterpenoids, several of which accumulate in response to abiotic stress. Here, we report discovery and functional characterization of a previously unrecognized monofunctional class I diterpene synthase (PvKSL1) viain vivoco‐expression assays with different copalyl pyrophosphate (CPP) isomers, structural and mutagenesis studies, as well as genomic and transcriptomic analyses. In particular, PvKSL1 convertsent‐CPP intoent‐abietadiene,ent‐palustradiene,ent‐levopimaradiene, andent‐neoabietadiene via a 13‐hydroxy‐8(14)‐ent‐abietene intermediate. Notably, although featuring a distinctent‐stereochemistry, this product profile is near‐identical to bifunctional (+)‐levopimaradiene/abietadiene synthases occurring in conifer trees. PvKSL1 has three of four active site residues previously shown to control (+)‐levopimaradiene/abietadiene synthase catalytic specificity. However, mutagenesis studies suggest a distinct catalytic mechanism in PvKSL1. Genome localization ofPvKSL1distant from other diterpene synthases, and its phylogenetic distinctiveness from known abietane‐forming diterpene synthases, support an independent evolution of PvKSL1 activity. Albeit at low levels,PvKSL1gene expression predominantly in roots suggests a role of diterpenoid formation in belowground tissue. Together, these findings expand the known chemical and functional space of diterpenoid metabolism in monocot crops.
Plant Sciences↗
SWIFT - Shaped-Waveform Interrogation of Fluids Technique
Researchers at Los Alamos have developed the Shaped-Waveform Interrogation of Fluids Technique (SWIFT), a technology enabling unprecedented process control capability. This technology provides important composition, flow rate, and pipe thickness measurements in real-time, at an extremely low cost and maintenance cycle. By utilizing a specialized shaped-waveform, as well as state-of-the-art signal processing, this information can be gathered without any frequency-domain signal processing, allowing for simplified electronics compared to nearly any other meter on the market. Since this technology is in expensive and non-invasive, with the capability of measuring liquids in both flow pipes and sealed containers, it is perfect for several industrial applications in the oil and gas, beverage, chemical, and pharmaceutical spaces. We are seeking a commercialization partner to license the technology or fund us through a Cooperative Research and Development Agreement (CRADA) to a further develop a usable product for commercial purposes.
47 OTHER INSTRUMENTATION↗
Tailoring Molecular Space to Navigate Phase Complexity in Cs-Based Quasi-2D Perovskites via Gated-Gaussian-Driven High-Throughput Discovery
Cesium-based quasi-2D halide perovskites (HPs) offer promising functionalities and low-temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi-2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high-throughput automated experimentation, the phase complexities in the molecular space of quasi-2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two-stage decision-making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary-compositional quasi-2D HP film realizing excellent optoelectronic functionalities is demonstrated. Finally, this work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems.
36 MATERIALS SCIENCE↗
Low-Pressure and Fluidized-Bed Chemical Vapor Deposition of PyC and ZrC for Space Nuclear Propulsion
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Lopez Honorato, Eddie↗
Detecting Sub-Micron Space Weathering Effects in Lunar Grains With Synchrotron Infrared Nanospectroscopy
Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1,500 –700 cm -1 (6.7–14.3 μm). Overall, our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space weathered bodies.
79 ASTRONOMY AND ASTROPHYSICS↗