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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Topologically-guided continuous protein crystallization controls bacterial surface layer self-assembly

Abstract Many bacteria and most archaea possess a crystalline protein surface layer (S-layer), which surrounds their growing and topologically complicated outer surface. Constructing a macromolecular structure of this scale generally requires localized enzymatic machinery, but a regulatory framework for S-layer assembly has not been identified. By labeling, superresolution imaging, and tracking the S-layer protein (SLP) from C. crescentus , we show that 2D protein self-assembly is sufficient to build and maintain the S-layer in living cells by efficient protein crystal nucleation and growth. We propose a model supported by single-molecule tracking whereby randomly secreted SLP monomers diffuse on the lipopolysaccharide (LPS) outer membrane until incorporated at the edges of growing 2D S-layer crystals. Surface topology creates crystal defects and boundaries, thereby guiding S-layer assembly. Unsupervised assembly poses challenges for therapeutics targeting S-layers. However, protein crystallization as an evolutionary driver rationalizes S-layer diversity and raises the potential for biologically inspired self-assembling macromolecular nanomaterials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Structure-guided design of pure orthosteric inhibitors of αIIbβ3 that prevent thrombosis but preserve hemostasis

A prevailing dogma is that inhibition of vascular thrombosis by antagonizing platelet integrin αIIbβ3 cannot be achieved without compromising hemostasis, thus causing serious bleeding and increased morbidity and mortality. It is speculated that these adverse outcomes result from drug-induced activating conformational changes in αIIbβ3 but direct proof is lacking. Here, we report the structure-guided design of peptide Hr10 and a modified form of the partial agonist drug tirofiban that act as “pure” antagonists of αIIbβ3, i.e., they no longer induce the conformational changes in αIIbβ3. Both agents inhibit human platelet aggregation but preserve clot retraction. Hr10 and modified tirofiban are as effective as partial agonist drugs in inhibiting vascular thrombosis in humanized mice, but neither causes serious bleeding, establishing a causal link between partial agonism and impaired hemostasis. Pure orthosteric inhibitors of αIIbβ3 may thus provide safer alternatives for human therapy, and valuable tools to probe structure–activity relationships in integrins.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Theory-guided design of high-entropy alloys with enhanced strength-ductility synergy

Abstract Metallic alloys have played essential roles in human civilization due to their balanced strength and ductility. Metastable phases and twins have been introduced to overcome the strength-ductility tradeoff in face-centered cubic (FCC) high-entropy alloys (HEAs). However, there is still a lack of quantifiable mechanisms to predict good combinations of the two mechanical properties. Here we propose a possible mechanism based on the parameter κ, the ratio of short-ranged interactions between closed-pack planes. It promotes the formation of various nanoscale stacking sequences and enhances the work-hardening ability of the alloys. Guided by the theory, we successfully designed HEAs with enhanced strength and ductility compared with other extensively studied CoCrNi-based systems. Our results not only offer a physical picture of the strengthening effects but can also be used as a practical design principle to enhance the strength-ductility synergy in HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Guided anisotropic oxygen transport in vacancy ordered oxides

Abstract Anisotropic and efficient transport of ions under external stimuli governs the operation and failure mechanisms of energy-conversion systems and microelectronics devices. However, fundamental understanding of ion hopping processes is impeded by the lack of atomically precise materials and probes that allow for the monitoring and control at the appropriate time- and length- scales. In this work, using in-situ transmission electron microscopy, we directly show that oxygen ion migration in vacancy ordered, semiconducting SrFeO 2.5 epitaxial thin films can be guided to proceed through two distinctly different diffusion pathways, each resulting in different polymorphs of SrFeO 2.75 with different ground electronic properties before reaching a fully oxidized, metallic SrFeO 3 phase. The diffusion steps and reaction intermediates are revealed by means of ab-initio calculations. The principles of controlling oxygen diffusion pathways and reaction intermediates demonstrated here may advance the rational design of structurally ordered oxides for tailored applications and provide insights for developing devices with multiple states of regulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Probing the limits of genetic recoding using multi-omics-guided evolution

Engineering the genetic code—by reassigning multiple of the 64 natural codons—enables making organisms resistant to all viruses, preventing genetic information exchange, and allowing the biosynthesis of genetically encoded unnatural polymers. However, synonymous codon replacement—recoding—is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using genome synthesis, directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple synthetic Escherichia coli genomes. We construct six partially recoded E. coli strains bearing up to 45.8% of a synthetic genome with a deleterious 57-codon genetic code. As our analyses revealed widespread defects—including unassigned codons in Syn61 and Syn57—we apply multi-omics to revise our genome design and mitigate defects. Using multi-omics, we show that recoding induces transcriptional and translational changes leading to fitness defects under hundreds of conditions. Finally, we develop a multi-omics-guided evolution strategy that rapidly restores fitness, enabling genome synthesis with radical changes.

Nyerges, Akos [Harvard Medical School, Boston, MA ↗
DOE OSTI2026

Resolving root causes of experiment discrepancies guided by machine learning

Abstract Scientists rely on accurate experimental data to explain nature and then harness this knowledge for applications addressing human needs. However, discrepancies between experiments of the same observable can impede scientific progress if one does not understand the underlying causes. Here, we developed a process that unravels data discrepancies by first using Bayesian machine learning to relate discrepancies to few of many, potentially biasing metadata features that encode experiment procedures. This machine learning output guides human experts to study discrepancy causes by simulating suspicious aspects of historical experiments or designing modern ones to address open questions. The study findings then lead to rejecting or correcting historical data on firm scientific bases. This process is demonstrated for the energy spectrum of neutrons emitted promptly (<1 ns) after fission of 252 Cf, a trusted nuclear physics Standard. It reduces the spread in experimental 252 Cf spectra by up to a factor of 6.

Neudecker, D. (ORCID:0000000339200627)↗
DOE OSTI2026

Machine-learning guided search for phonon-mediated superconductivity in boron and carbon compounds

We present a workflow that iteratively combines ab-initio calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which have been largely overlooked in previous studies. Electron-phonon coupling (EPC) properties and critical temperature (T c ) of 417 boron, carbon, and borocarbide compounds have been calculated with density functional perturbation theory (DFPT) and isotropic Eliashberg approximation. Our study addresses T c convergence of Brillouin zone sampling with an ansatz test, stabilizing imaginary phonon modes for significant EPC contributions, and comparing the performance of two ML models, especially when including compounds of dynamical instability. We predict a few promising superconducting compounds with formation energy just above the ground state convex hull, such as Ca 5 B 3 N 6 (35 K), TaNbC 2 (28.4 K), Nb 3 B 3 C (16.4 K), Y 2 B 3 C 2 (4.0 K), Pd 3 CaB (7.0 K), MoRuB 2 (15.6 K), RuVB 2 (15.0 K), RuSc 3 C 4 (6.6 K) among others.

Nepal, Niraj K. [Ames Laboratory (AMES), Ames, IA ↗
DOE OSTI2022

A single-atom library for guided monometallic and concentration-complex multimetallic designs

Atomically dispersed single-atom catalysts have the potential to bridge heterogeneous and homogeneous catalysis. Dozens of single-atom catalysts have been developed, and they exhibit notable catalytic activity and selectivity that are not achievable on metal surfaces. Although promising, there is limited knowledge about the boundaries for the monometallic single-atom phase space, not to mention multimetallic phase spaces. Here, single-atom catalysts based on 37 monometallic elements are synthesized using a dissolution-and-carbonization method, characterized and analyzed to build the largest reported library of single-atom catalysts. In conjunction with in situ studies, we uncover unified principles on the oxidation state, coordination number, bond length, coordination element and metal loading of single atoms to guide the design of single-atom catalysts with atomically dispersed atoms anchored on N-doped carbon. We utilize the library to open up complex multimetallic phase spaces for single-atom catalysts and demonstrate that there is no fundamental limit on using single-atom anchor sites as structural units to assemble concentration-complex single-atom catalyst materials with up to 12 different elements. Furthermore, our work offers a single-atom library spanning from monometallic to concentration-complex multimetallic materials for the rational design of single-atom catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computation-guided optimization of split protein systems

Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. Furthermore, this new technology will streamline the generation and use of split protein systems for diverse applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The nascent RNA binding complex SFiNX licenses piRNA-guided heterochromatin formation

The PIWI-interacting RNA (piRNA) pathway protects genome integrity in part through establishing repressive heterochromatin at transposon loci. Silencing requires piRNA-guided targeting of nuclear PIWI proteins to nascent transposon transcripts, yet the subsequent molecular events are not understood. In this work, we identify SFiNX (silencing factor interacting nuclear export variant), an interdependent protein complex required for Piwi-mediated cotranscriptional silencing in Drosophila . SFiNX consists of Nxf2–Nxt1, a gonad-specific variant of the heterodimeric messenger RNA export receptor Nxf1–Nxt1 and the Piwi-associated protein Panoramix. SFiNX mutant flies are sterile and exhibit transposon derepression because piRNA-loaded Piwi is unable to establish heterochromatin. Within SFiNX, Panoramix recruits heterochromatin effectors, while the RNA binding protein Nxf2 licenses cotranscriptional silencing. Our data reveal how Nxf2 might have evolved from an RNA transport receptor into a cotranscriptional silencing factor. Thus, NXF variants, which are abundant in metazoans, can have diverse molecular functions and might have been coopted for host genome defense more broadly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

An informatics guided classification of miscible and immiscible binary alloy systems

The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles (HTFP) calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Impressively, the classification by the miscibility map yields a robust validation on the capability of the well-known Miedema’s theory (95% agreement) and shows good agreement with the HTFP method (90% agreement). Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Structure-guided Discovery of Dual-recognition Chemibodies

Small molecules and antibodies each have advantages and limitations as therapeutics. Here, we present for the frst time to our knowledge, the structure-guided design of “chemibodies” as small molecule-antibody hybrids that ofer dual recognition of a single target by both a small molecule and an antibody, using DPP-IV enzyme as a proof of concept study. Biochemical characterization demonstrates that the chemibodies present superior DPP-IV inhibition compared to either small molecule or antibody component alone. We validated our design by successfully solving a co-crystal structure of a chemibody in complex with DPP-IV, confrming specifc binding of the small molecule portion at the interior catalytic site and the Fab portion at the protein surface. The discovery of chemibodies presents considerable potential for novel therapeutics that harness the power of both small molecule and antibody modalities to achieve superior specifcity, potency, and pharmacokinetic properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Azobenzene-based sinusoidal surface topography drives focal adhesion confinement and guides collective migration of epithelial cells

Surface topography is a key parameter in regulating the morphology and behavior of single cells. At multicellular level, coordinated cell displacements drive many biological events such as embryonic morphogenesis. However, the effect of surface topography on collective migration of epithelium has not been studied in detail. Mastering the connection between surface features and collective cellular behaviour is highly important for novel approaches in tissue engineering and repair. Herein, we used photopatterned microtopographies on azobenzene-containing materials and showed that smooth topographical cues with proper period and orientation can efficiently orchestrate cell alignment in growing epithelium. Furthermore, the experimental system allowed us to investigate how the orientation of the topographical features can alter the speed of wound closure in vitro. Our findings indicate that the extracellular microenvironment topography coordinates their focal adhesion distribution and alignment. These topographic cues are able to guide the collective migration of multicellular systems, even when cell–cell junctions are disrupted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training

Abstract Building heating, ventilation, and air conditioning (HVAC) systems account for nearly half of building energy consumption and $$20\%$$ of total energy consumption in the US. Their operation is also crucial for ensuring the physical and mental health of building occupants. Compared with traditional model-based HVAC control methods, the recent model-free deep reinforcement learning (DRL) based methods have shown good performance while do not require the development of detailed and costly physical models. However, these model-free DRL approaches often suffer from long training time to reach a good performance, which is a major obstacle for their practical deployment. In this work, we present a systematic approach to accelerate online reinforcement learning for HVAC control by taking full advantage of the knowledge from domain experts in various forms . Specifically, the algorithm stages include learning expert functions from existing abstract physical models and from historical data via offline reinforcement learning, integrating the expert functions with rule-based guidelines, conducting training guided by the integrated expert function and performing policy initialization from distilled expert function. Moreover, to ensure that the learned DRL-based HVAC controller can effectively keep room temperature within the comfortable range for occupants, we design a runtime shielding framework to reduce the temperature violation rate and incorporate the learned controller into it. Experimental results demonstrate up to 8.8 X speedup in DRL training from our approach over previous methods, with low temperature violation rate.

Xu, Shichao↗
DOE OSTI2018

Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

The electrochemical reduction of CO2 and H2 evolution from water can be used to store renewable energy that is produced intermittently. Scale-up of these reactions requires the discovery of effective electrocatalysts, but the electrocatalyst search space is too large to explore exhaustively. Here we present a theoretical, fully automated screening method that uses a combination of machine learning and optimization to guide density functional theory calculations, which are then used to predict electrocatalyst performance. We demonstrate the feasibility of this method by screening various alloys of 31 different elements, and thereby perform a screening that encompasses 50% of the d-block elements and 33% of the p-block elements. This method has thus far identified 131 candidate surfaces across 54 alloys for CO2 reduction and 258 surfaces across 102 alloys for H2 evolution. We use qualitative analyses to prioritize the top candidates for experimental validation.

Chemistry↗
DOE OSTI2022

Structure-guided functional studies of plasmid-encoded dihydrofolate reductases reveal a common mechanism of trimethoprim resistance in Gram-negative pathogens

Two plasmid-encoded dihydrofolate reductase (DHFR) isoforms, DfrA1 and DfrA5, that give rise to high levels of resistance in Gram-negative bacteria were structurally and biochemically characterized to reveal the mechanism of TMP resistance and to support phylogenic groupings for drug development against antibiotic resistant pathogens. Preliminary screening of novel antifolates revealed related chemotypes that showed high levels of inhibitory potency against Escherichia coli chromosomal DHFR (EcDHFR), DfrA1, and DfrA5. Kinetics and biophysical analysis, coupled with crystal structures of trimethoprim bound to EcDHFR, DfrA1 and DfrA5, and two propargyl-linked antifolates (PLA) complexed with EcDHFR, DfrA1 and DfrA5, were determined to define structural features of the substrate binding pocket and guide synthesis of pan-DHFR inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Abstract Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.

Chen, Jiadong (ORCID:0009000476038838)↗