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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Controlled Solidification of Liquids Within the SFC Primary Phase Field of the “Fe{sub 2}O{sub 3}”-CaO-SiO{sub 2} System in Air

To provide fundamental information on the phases and microstructures formed during sintering, a liquid with a bulk composition within the silico ferrite of calcium (SFC) primary phase field in the ternary “Fe{sub 2}O{sub 3}”-CaO-SiO{sub 2} system in air was solidified at a controlled rate. Samples of a bulk composition with a CaO/SiO{sub 2} ratio of 4.00 and 69.24 wt pct Fe{sub 2}O{sub 3}, were cooled from 1623 K (1350 °C) at 2 K/s, with samples quenched at temperatures between 1513 K (1240 °C) to 1453 K (1180 °C). The silico ferrite of calcium and aluminium I (SFCA-I) and Ca{sub 7.2}Fe{sub 0.8}{sup 2+}Fe{sub 30}{sup 3+}O{sub 53} phases were observed to form an intergrowth (‘SFC-I’) rather than the anticipated SFC phase. Solidification was found to occur in three stages, Liquid + ‘SFC-I’, Liquid + ‘SFC-I’ + C{sub 2}S + CF{sub 2}, and C{sub 2}S + CF{sub 2} + CF, where C{sub 2}S denotes dicalcium silicate, CF denotes calcium ferrite and CF{sub 2} denotes calcium diferrite. The phases formed and the solidification sequence differ from those predicted under equilibrium and Scheil–Gulliver Cooling. Although not directly applicable to industrial operations, this research clearly shows that the formation of both the SFCA and SFCA-I phase in iron ore sinters is controlled by kinetic processes rather than equilibrium conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A novel approach to build algal consortia for sustainable biomass production

In the last decade, microalgae have reemerged as a feedstock for biofuels and a diverse suite of bioproducts. Yet, considerable challenges must be overcome before algal biofuels and bioproducts become technoeconomically viable. At present, single algal strain selected for particular phenotypic traits, such as maximum specific growth rate or lipid content, are commonly scaled for cultivation in open, outdoor raceway ponds due to the low capital costs of these systems. Although this monoculture approach may maximize the production of end products, monocultures are particularly susceptible to crashes associated with environmental and biological variability. An approach that has been proposed to generate more productive and stable microalgal crops is the use of eco-engineered communities, or consortia. Yet, attempts to construct productive consortia have not been consistently successful. We argue that failures stem from the lack of an eco-engineering approach to design species combinations. Here, we used an in silico method to build consortia before testing their performance against monocultures. Focusing on consortia of Nannochloropsis and Microchloropsis, we measured growth of strains along gradients of light, temperature, and salinity and used a functional dispersion approach to generate over 8000 functionally-diverse consortia combinations. We tested the 50 most functionally diverse consortia in a laboratory experiment and found that consortia overwhelmingly outperformed monocultures. Indeed, overyielding (OY) and a positive net biodiversity effect (NBE) was found respetively in 8%-86% and 88-92% of consortia combinations over the different experimental phases. To our knowledge, this is the first application of an in silico approach to design functionally diverse consortia before laboratory and field testing. Furthermore, our results highlight the importance of employing a functional diversity approach for consortia design.

09 BIOMASS FUELS↗
DOE OSTI2022

Deconvoluting the benefits of porosity distribution in layered electrodes on the electrochemical performance of Li-ion batteries

Thick Lithium Ion Battery (LIB) electrodes suffer from poor rate capability and high ionic impedance due to their thickness and mesostructure. Therefore, optimizing thick electrode architectures becomes crucial. In this work, we report a systematic assessment of the ionic resistance in heterogeneous porous electrodes through the combination of computational simulations using a 4D-resolved model and experimental measurements. The first part of the study is devoted to a general assessment of Electrochemical Impedance Spectroscopy (EIS) spectra, mapping the impact of ionic and electronic resistances on the overall impedances of uncalendered and calendered LiNi 1/3 Mn 1/3 Co 1/3 O 2 , LiFePO 4 and graphite electrodes. In the second part, in silico-generated electrodes with different porosities are used in computational EIS simulations to analyze the impact of the electrode porosity on the ionic impedance. As expected, the results show that a lower porosity leads to a higher ionic impedance because of a higher electrode tortuosity factor. Furthermore, in silico-generated electrodes with different porosities were stacked and assembled to create heterogeneities of porosity along the thickness, and used in computational EIS and galvanostatic discharge simulations. The computational results show that the porosity heterogeneity along the electrode thickness has a significant effect on the ionic impedance and capacity of the electrode. The electrode architecture with progressively decreasing porosity from separator to current collector shows the highest performance, a trend validated by our in house experimental EIS and galvanostatic discharge also reported in this manuscript. Overall, we conclude that the ionic resistance in a thick electrode can be effectively reduced through proper tuning of the porosity heterogeneity. The proposed heterogeneous electrode architectures presented here could enormously help building efficient thick electrodes for LIBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Automated bidirectional coupling of multiscale models of aerosol dosimetry: Validation with subject-specific deposition data

Assessing the toxicity of airborne particulate matter or the efficacy of inhaled drug depends upon accurate estimates of deposited fraction of inhaled materials. In silico approaches can provide important insights into site- or airway-specific deposition of inhaled aerosols in the respiratory system. In this study, we improved on our recently developed 3D/1D model that simulate aerosol transport and deposition in the whole lung over multiple breath cycles. A subject-specific multiscale lung model of a healthy male subject using computational fluid-particle dynamics (CFPD) in a 3D model of the oral cavity through the large bronchial airways entering each lobe was bidirectionally coupled with a recently improved Multiple Path Particle Dosimetry (MPPD) model to predict aerosol deposition over the entire respiratory tract over multiple breaths for four conditions matching experimental aerosol exposures in the same subject from which the model was developed. These include two particle sizes (1 and 2.9 μm) and two subject-specific breathing rates of ~300 ml/s (slow breathing) and ~750 ml/s (fast breathing) at a target tidal volume of 1 L. In silico predictions of retained fraction were 0.31 and 0.29 for 1 μm and 0.66 and 0.62 for 2.9 μm during slow and fast breathing, respectively, and compared well with experimental data (1 μm: 0.31 ± 0.01 (slow) and 0.27 ± 0.01 (fast), 2.9 μm: 0.63 ± 0.03 (slow) and 0.68 ± 0.02 (fast)). Furthermore, these results provide a great deal of confidence in the validity and reliability of our approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Molecular chaperone RAP interacts with LRP1 in a dynamic bivalent mode and enhances folding of ligand-binding regions of other LDLR family receptors

The low-density lipoprotein receptor (LDLR) family of receptors are cell-surface receptors that internalize numerous ligands and play crucial role in various processes, such as lipoprotein metabolism, hemostasis, fetal development, etc. Previously, receptor-associated protein (RAP) was described as a molecular chaperone for LDLR-related protein 1 (LRP1), a prominent member of the LDLR family. We aimed to verify this role of RAP for LRP1 and two other LDLR family receptors, LDLR and vLDLR, and to investigate the mechanisms of respective interactions using a cell culture model system, purified system, and in silico modelling. Upon coexpression of RAP with clusters of the ligand-binding complement repeats (CRs) of the receptors in secreted form in insect cells culture, the isolated proteins had increased yield, enhanced folding, and improved binding properties compared with proteins expressed without RAP, as determined by circular dichroism and surface plasmon resonance. Within LRP1 CR-clusters II and IV, we identified multiple sites comprised of adjacent CR doublets, which provide alternative bivalent binding combinations with specific pairs of lysines on RAP. Mutational analysis of these lysines within each of isolated RAP D1/D2 and D3 domains having high affinity to LRP1 and of conserved tryptophans on selected CR-doublets of LRP1, as well as in silico docking of a model LRP1 CR-triplet with RAP, indicated a universal role for these residues in interaction of RAP and LRP1. Consequently, we propose a new model of RAP interaction with LDLR family receptors based on switching of the bivalent contacts between molecules over time in a dynamic mode.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

PET-FBA: A lightweight enzyme allocation and thermodynamics-constrained flux analysis approach to explore Escherichia coli metabolic adaptation to intracellular acidification

Escherichia coli employs diverse strategies to adapt to acidic environments that disrupt enzyme activity and the thermodynamic feasibility of essential reactions. To understand the impact of pH stress on cell metabolism, we present the PET-FBA (pH-, Enzyme protein allocation-, and Thermodynamics-constrained Flux Balance Analysis) framework. PET-FBA extends genome-scale modeling by integrating enzyme protein costs and reaction Gibbs free energy changes. Additionally, by incorporating pH-dependent enzyme kinetics in response to intracellular acidification, this framework enables the simulation of E. coli's metabolic adjustments across varying external pH levels. The model's accuracy is validated by comparing in silico growth simulations with experimental measurements under both anaerobic and aerobic conditions, as well as in silico gene knockouts of essential genes. By explicitly incorporating pH effects, our model accurately replicates the metabolic shift towards lactate production as the primary fermentation product at low pH in anaerobic conditions. This shift is only predicted when enzyme kinetics are dynamically adjusted as a function of pH. Further analysis revealed that this shift can be attributed to the reduced protein efficiency of the acetyl-CoA branch compared to lactate dehydrogenase under acidic stress, which then becomes crucial for maintaining NAD regeneration and cell growth at low pH. Furthermore, we identified strategies for enhancing cell growth under acidic anaerobic conditions by improving the enzyme activity of lactate dehydrogenase and pyruvate formate lyase, which increases NAD production efficiency and reduces enzyme protein allocation costs. Designed as a lightweight yet versatile framework, PET-FBA enables efficient genome-scale metabolic analysis. Using E. coli as a model system, our framework provides a systematic approach to understanding metabolic responses to environmental stress, pinpointing key metabolic bottlenecks, and identifying potential targets for strain optimization.

42 ENGINEERING↗
DOE OSTI2021

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics and exposomics studies is to fully characterize the small molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversi-ty present within the metabolome and exposome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemis-try methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alter-native route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solu-tion. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectros-copy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics and exposomics studies. We expect this review will inspire researchers in the field of small molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Metabolism↗
DOE OSTI2020

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

CyProduct: A software tool for accurately predicting the byproducts of human cytochrome P450 metabolism

In silico metabolism prediction is a cheminformatic task of autonomously predicting the set of metabolic byproducts produced from a specified molecule and a set of enzymes or reactions. Here we describe a novel machine-learned in silico cytochrome P450 (CYP450) metabolism prediction suite, called CyProduct, that accurately predicts metabolic byproducts for a specified molecule and a human CYP450 isoform. It includes three modules: (1) CypReact, a tool that predicts if the query compound reacts with a given CYP450 enzyme; (2) CypBoM, a tool that accurately predicts the “bond site” of the reaction (i.e., which specific bonds within the query molecule react with the CYP isoform); and (3) MetaboGen, a tool that generates the metabolic byproducts based on CypBoM’s bond-site prediction. CyProduct predicts metabolic biotransformation products for each of the nine most important human CYP450 enzymes. CypBoM uses an important new concept called “Bond of Metabolism” (BoM), which extends the traditional “Site of Metabolism" (SoM) by specifying the information about the set of chemical bonds that is modified or formed in a metabolic reaction (rather than the specific atom). We created a BoM database for 3487 CYP450-mediated Phase I reactions, then used this to train the CypBoM Predictor to predict the reactive bond locations on substrate molecules. CypBoM Predictor’s cross-validated Jaccard score for reactive bond prediction ranged from 0.380 to 0.452 over the nine CYP450 enzymes. Over variants of a test set of 72 known CYP450 substrates and 30 non-reactants, CyProduct outperformed the other packages -- including ADMET Predictor, BioTransformer and GLORY -- by an average of 200% (wrt Jaccard score) in terms of predicting metabolites. The CyProduct suite and the datasets are freely available at https://bitbucket.org/wishartlab/cyproduct/src/master/.

Machine learning, Cytochrome P450, Metabolism pred↗
DOE OSTI2021

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl

The in silico modeling of molten salts is critical for emerging "carbon-free" energy applications but is inhibited by the cost of quantum mechanically treating the high polarizabilities of molten salts. Here, we integrate configurational sampling using classical force fields with active learning to automate and accelerate the generation of Gaussian approximation potentials (GAP) for molten salts. This methodology reduces the number of expensive ab initio evaluations required for training set generation to O(100), enabling the facile parametrization of a molten LiCl GAP model that exhibits a 19 000-fold speedup relative to AIMD. The developed molten LiCl GAP model is applied to sample extended spatiotemporal scales, permitting new physical insights into molten LiCl's coordination structure as well as experimentally validated predictions of structures, densities, self-diffusion constants, and ionic conductivities. The developed methodology significantly lowers the barrier to the in silico understanding and design of molten salts across the periodic table.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Architector for high-throughput cross-periodic table 3D complex building

Abstract Rare-earth and actinide complexes are critical for a wealth of clean-energy applications. Three-dimensional (3D) structural generation and prediction for these organometallic systems remains a challenge, limiting opportunities for computational chemical discovery. Here, we introduce Architector , a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes capable of capturing nearly the full diversity of the known experimental chemical space. Beyond known chemical space, Architector performs in-silico design of new complexes including any chemically accessible metal-ligand combinations. Architector leverages metal-center symmetry, interatomic force fields, and tight binding methods to build many possible 3D conformers from minimal 2D inputs including metal oxidation and spin state. Over a set of more than 6,000 x-ray diffraction (XRD)-determined complexes spanning the periodic table, we demonstrate quantitative agreement between Architector-predicted and experimentally observed structures. Further, we demonstrate out-of-the box conformer generation and energetic rankings of non-minimum energy conformers produced from Architector , which are critical for exploring potential energy surfaces and training force fields. Overall, Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deep learning for characterizing the self-assembly of three-dimensional colloidal systems

Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems’ stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. In this work, we first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages

Damage in the microstructures of energetic materials (EMs), such as propellants and plastic bonded explosives (PBXs), can significantly alter their response to external loads. Both sensitization and desensitization can occur, causing concerns with safety and performance in the field; predictive models that connect damage and the sensitivity of EMs can enable design and provide confidence in their robustness and reliability. However, modeling of damage evolution is challenging for real microstructures of EMs; samples of damaged EMs are difficult to obtain, thereby hindering experiments and direct numerical simulations to determine the sensitivity of EMs at various stages of damage. Here, we develop an approach to generate synthetic, i.e., in silico produced, damaged microstructures for use in simulations to connect damage levels to sensitivity. The development of the present workflow to generate and impose varying levels of damage in microstructures, known as HEDS (Heterogeneous Energetic Material Damage Simulator), begins with a small set of images of damaged PBXs and combines a collection of deep neural network techniques to generate microstructures with varying levels of damage. By making the synthetic microstructures conform closely to those observed in available real, imaged microstructures, we develop an ensemble of damaged microstructures that can be used for in silico shock experiments. HEDS develops these microstructure ensembles as level set fields, which are directly employed in a sharp interface Eulerian hydrocode where shock simulations are performed to quantify the energy release rate from hotspot fields generated in the microstructure. These capabilities can be useful for the analysis and assessment of changes in the sensitivity of EMs and to design formulations that are less susceptible to damage-induced changes in sensitivity and performance.

Fang, Irene (ORCID:0009000844557122)↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Paraburkholderia madseniana sp. nov., a phenolic acid-degrading bacterium isolated from acidic forest soil

RP11 T was isolated from forest soil following enrichment with 4-hydroxybenzoic acid. Cells of RP11 T are aerobic, non-sporulating, exhibit swimming motility, and are rods (0.8 µm by 1.4 µm) that often occur as diplobacillus or in short chains (3–4 cells). Optimal growth on minimal media containing 4-hydroxybenzoic acid (µ=0.216 hr -1 ) occurred at 30 °C, pH 6.5 or 7.0 and 0% salinity. Comparative chemotaxonomic, genomic and phylogenetic analyses revealed the isolate was distinct from its closest relative type strains identified as Paraburkholderia aspalathi LMG 27731 T , Paraburkholderia fungorum LMG 16225 T and Paraburkholderia caffeinilytica CF1 T . Strain RP11 T is genetically distinct from P. aspalathi , its closest relative, in terms of 16S rRNA gene sequence similarity (98.7%), genomic average nucleotide identity (94%) and in silico DNA–DNA hybridization (56.7%±2.8). The composition of fatty acids and substrate utilization pattern differentiated strain RP11 T from its closest relatives, including growth on phthalic acid. Strain RP11 T encoded the greatest number of aromatic degradation genes of all eleven closely related type strains and uniquely encoded a phthalic acid dioxygenase and paralog of the 3-hydroxybenzoate 4-monooxygenase. The only ubiquinone detected in strain RP11 T was Q-8, and the major cellular fatty acids were C 16:0 , 3OH-C 16:0 , C 17:0 cyclo, C 19:0 cyclo ω8c, and summed feature 8 (C 18:1 ω7c/ω6c). On the basis of this polyphasic approach, it was determined that strain RP11 T represents a novel species from the genus Paraburkholderia for which the name Paraburkholderia madseniana sp. nov. is proposed. The type strain is RP11 T (=DSM 110123 T =LMG 31517 T ).

Microbiology↗
DOE OSTI2020

Paraburkholderia solitsugae sp. nov. and Paraburkholderia elongata sp. nov., phenolic acid-degrading bacteria isolated from forest soil and emended description of Paraburkholderia madseniana

Two bacterial strains, 1N T and 5N T , were isolated from hemlock forest soil using a soluble organic matter enrichment. Cells of 1N T (0.65×1.85 µm) and 5N T (0.6×1.85 µm) are Gram-stain-negative, aerobic, motile, non-sporulating and exist as single rods, diplobacilli or in chains of varying length. During growth in dilute media (≤0.1× tryptic soy broth; TSB), cells are primarily motile with flagella. At higher concentrations (≥0.3× TSB), cells of both strains increasingly form non-motile chains, and cells of 5N T elongate (0.57×~7 µm) and form especially long filaments. Optimum growth of 1N T and 5N T occurred at 25–30 °C, pH 6.5–7.0 and <0.5% salinity. Results of comparative chemotaxonomic, genomic and phylogenetic analyses revealed that 1N T and 5N T were distinct from one another and their closest related type strains: Paraburkholderia madseniana RP11 T , Paraburkholderia aspalathi LMG 27731 T and Paraburkholderia caffeinilytica CF1 T . The genomes of 1N T and 5N T had an average nucleotide identity (91.6 and 91.3%) and in silico DNA–DNA hybridization values (45.8%±2.6 and 45.5%±2.5) and differed in functional gene content from their closest related type strains. The composition of fatty acids and patterns of substrate use, including the catabolism of phenolic acids, also differentiated strains 1N T and 5N T from each other and their closest relatives. The only ubiquinone present in strains 1N T and 5N T was Q-8. The major cellular fatty acids were C 16 : 0 , 3OH-C 16 : 0 , C 17 : 0 cyclo, C 19 : 0 cyclo ω8 c and summed features 2 (3OH-C 14 : 0 / C 16 : 1 iso I), 3 (C 16 : 1 ω6 c /ω7 c ) and 8 (C 18 : 1 ω7 c /ω6 c ). A third bacterium, strain RL16-012-BIC-B, was isolated from soil associated with shallow roots and was determined to be a strain of P. madseniana (ANI, 98.8%; 16S rRNA gene similarity, 100%). Characterizations of strain RL16-012-BIC-B (DSM 110723=LMG 31706) led to proposed emendments to the species description of P. madseniana . Our polyphasic approach demonstrated that strains 1N T and 5N T represent novel species from the genus Paraburkholderia for which the names Paraburkholderia solitsugae sp. nov. (type strain 1N T =DSM 110721 T =LMG 31704 T ) and Paraburkholderia elongata sp. nov. (type strain 5N T =DSM 110722 T =LMG 31705 T ) are proposed.

Microbiology↗
DOE OSTI2022

Demonstration of Decentralized Physics-Driven Learning

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain, neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine-learning tasks from local evolution rules. However, to date, such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here, we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this method, our system effectively trains itself, optimizing its resistance values without the use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. Here, we demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks, where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗