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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quality control of mislocalized and orphan proteins

A healthy and functional proteome is essential to cell physiology. However, this is constantly being challenged as most steps of protein metabolism are error-prone and changes in the physico-chemical environment can affect protein structure and function, thereby disrupting proteome homeostasis. Among a variety of potential mistakes, proteins can be targeted to incorrect compartments or subunits of protein complexes may fail to assemble properly with their partners, resulting in the formation of mislocalized and orphan proteins, respectively. Quality control systems are in place to handle these aberrant proteins, and to minimize their detrimental impact on cellular functions. Here, we discuss recent findings on quality control mechanisms handling mislocalized and orphan proteins. We highlight common principles involved in their recognition and summarize how accumulation of these aberrant molecules is associated with aging and disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A Novel Non‐Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Abstract Amino‐acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

Chemistry↗
NASA NTRS1996

A Revolute Joint With Linear Load-Displacement Response for Precision Deployable Structures

NASA Langley Research center is developing key structures and mechanisms technologies for micron-accuracy, in-space deployment of future space instruments. Achieving micron-accuracy deployment requires significant advancements in deployment mechanism design such as the revolute joint presented herein. The joint presented herein exhibits a load-cycling response that is essentially linear with less than two percent hysteresis, and the joint rotates with less than one in.-oz. of resistance. A prototype reflector metering truss incorporating the joint exhibits only a few microns of kinematic error under repeated deployment and impulse loading. No other mechanically deployable structure found in literature has been demonstrated to be this kinematically accurate.

Lake, Mark S.↗
DOE OSTI2021

AtomSets as a hierarchical transfer learning framework for small and large materials datasets

Abstract Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (<400) and large structural data (>130,000). The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.

Chen, Chi (ORCID:0000000180087043)↗
DOE OSTI2022

Group structure selection with random forests

Choosing an appropriate group structure for multigroup transport is far from an exact science. For some applications, one blindly uses a group structure developed years ago by forgotten methods. Furthermore, one sometimes uses the same group structure for a variety of problems, even if the group structure was originally developed with a certain application in mind. In this work, we create optimized group structures with simulated annealing for critical assembly test problems and apply a random forest regressor with bagging to choose the best group structure based on parameters of the different test problems. The optimized group structures were generated using a simulated annealing optimizer for several simple, spherical, and unreflected problems. The optimization was performed to minimize a cost function that included fission rate, absorption rate, leakage, and k{sub eff}. A random forest regressor was then trained on a set of International Criticality Safety Benchmark Evaluation Project inputs and used to select one of these six group structures. The trained machine learning model chose the best group structure 65% of the time, and one of the three best 89% of the time. Furthermore, it decreased the L2 error over all test problems by a factor of 25 when compared to the standard Los Alamos 70-group structure. In other words, the model chose group structure that were far more appropriate for the test problems than the traditional LANL group structure. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Structural damage assessment as an identification problem

Damage assessment of structural assemblies is treated as an identification problem. A brief review of identification methods is first presented with particular focus on the output error approach. The use of numerical optimization methods in identifying the location and extent of damage in structures is studied. The influence of damage on eigenmode shapes and static displacements is explored as a means of formulating a measure of damage in the structure. Preliminary results obtained in this study are presented and special attention is directed at the shortcomings associated with the nonlinear programming approach to solving the optimization problem.

Hajela, Prabhat↗
NASA NTRS2007

Build-Up Approach to Updating the Mock Quiet Spike Beam Model

When a new aircraft is designed or a modification is done to an existing aircraft, the aeroelastic properties of the aircraft should be examined to ensure the aircraft is flight worthy. Evaluating the aeroelastic properties of a new or modified aircraft can include performing a variety of analyses, such as modal and flutter analyses. In order to produce accurate results from these analyses, it is imperative to work with finite element models (FEM) that have been validated by or correlated to ground vibration test (GVT) data, Updating an analytical model using measured data is a challenge in the area of structural dynamics. The analytical model update process encompasses a series of optimizations that match analytical frequencies and mode shapes to the measured modal characteristics of structure. In the past, the method used to update a model to test data was "trial and error." This is an inefficient method - running a modal analysis, comparing the analytical results to the GVT data, manually modifying one or more structural parameters (mass, CG, inertia, area, etc.), rerunning the analysis, and comparing the new analytical modal characteristics to the GVT modal data. If the match is close enough (close enough defined by analyst's updating requirements), then the updating process is completed. If the match does not meet updating-requirements, then the parameters are changed again and the process is repeated. Clearly, this manual optimization process is highly inefficient for large FEM's and/or a large number of structural parameters. NASA Dryden Flight Research Center (DFRC) has developed, in-house, a Mode Matching Code that automates the above-mentioned optimization process, DFRC's in-house Mode Matching Code reads mode shapes and frequencies acquired from GVT to create the target model. It also reads the current analytical model, as we11 as the design variables and their upper and lower limits. It performs a modal analysis on this model and modifies it to create an updated model that has similar mode shapes and frequencies as those of the target model. The Mode Matching Code output frequencies and modal assurance criteria (MAC) values that allow for the quantified comparison of the updated model versus the target model. A recent application of this code is the F453 supersonic flight testing platform, NASA DFRC possesses a modified F-15B that is used as a test bed aircraft for supersonic flight experiments. Traditionally, the finite element model of the test article is generated. A GVT is done on the test article ta validate and update its FEM. This FEM is then mated to the F-15B model, which was correlated to GVT data in fall of 2004, A GVT is conducted with the test article mated to the aircraft, and this mated F-15B/ test article FEM is correlated to this final GVT.

Herrera, Claudia Y.↗
DOE OSTI2024

Randomized measurement protocols for lattice gauge theories

Randomized measurement protocols, including classical shadows, entanglement tomography, and randomized benchmarking are powerful techniques to estimate observables, perform state tomography, or extract the entanglement properties of quantum states. While unraveling the intricate structure of quantum states is generally difficult and resource-intensive, quantum systems in nature are often tightly constrained by symmetries. Furthermore, this can be leveraged by the symmetry-conscious randomized measurement schemes we propose, yielding clear advantages over symmetry-blind randomization such as reducing measurement costs, enabling symmetry-based error mitigation in experiments, allowing differentiated measurement of (lattice) gauge theory entanglement structure, and, potentially, the verification of topologically ordered states in existing and near-term experiments. Crucially, unlike symmetry-blind randomized measurement protocols, these latter tasks can be performed without relearning symmetries via full reconstruction of the density matrix.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

An end-to-end pointing budget approach to planetary observing systems with application to EOS

Previously published error budgets have focused on spacecraft error sources for pointing error and have tended to include only spacecraft pointing rather than the ultimate geolocation of each pixel of dam onto a well-defined spot on the surface of the Earth. A systematic approach to geolocation error budgeting, including all contributors in the geolocation process is presented. Its structure allows simultaneous expression of the needs of instrument teams as well as spacecraft design teams. It allows explicit acknowledgement of approximations made for on-board control as well as the ultimate geolocation accuracy achievable after ground processing and exploits the commonality inherent in the on-board and post-processing error budgets. It also includes the uncontrolled and unmeasured spacecraft jitter. This approach can be used to investigate the mission-wide benefit of a variety of design choices, (such as on-board sensing and ground correction of measurements contrasted with on-board correction of measurements). Additionally, in light of increasing accuracy requirements as sensor resolution improves, numerous non-spacecraft contributors to geolocation error are quantified.

Throckmorton, A.↗
NASA NTRS1977

Industrial Productivity

NASTRAN is an offshoot of the computer-design technique used in construction of airplanes and spacecraft. [n this technique engineers create a mathematical model of the aeronautical or space vehicle and "fly" it on the ground by means of computer simulation. The technique enables them to study performance and structural behavior of a number of different designs before settling on the final configuration and proceeding with construction. From this base of aerospace experience, NASA-Goddard developed the NASTRAN general purpose computer program, which offers an exceptionally wide range of analytic capability with regard to structures. NASTRAN has been applied to autos, trucks, railroad cars, ships, nuclear power reactors, steam turbines, bridges, and office buildings. NASA-Langley provides program maintenance services regarded as vital by many NASTRAN users. NASTRAN is essentially a predictive tool. It takes an electronic look at a computerire$.dedgn and reports how the structure will react under a great many different conditions. It can, for example, note areas where high stress levels will occur-potential failure points that need strengthening. Conversely, it can identify over-designed areas where weight and material might be saved safely. NASTRAN can tell how pipes stand up under strong fluid flow, how metals are affected by high temperatures, how a building will fare in an earthquake or how powerful winds will cause a bridge to oscillate. NASTRAN analysis is quick and inexpensive. It minimizes trial-and-error in the design process and makes possible better, safe, lighter structures affording large-scale savings in development time and materials. Some examples of the broad utility NASTRAN is finding among industrial firms are shown on these pages.

Source record↗
NASA NTRS1991

MacPASCO - A Macintosh-based, interactive graphic preprocessor for structural analysis and sizing

MacPASCO, an interactive, graphic preprocessor for panel design is described. MacPASCO creates input for PASCO, an existing computer code for structural analysis and optimization of longitudinal stiffened composite panels. By using a graphical user interface, MacPASCO simplifies the specification of panel geometry and reduces user input errors, thus making the modeling and analysis of panel designs more efficient. The user draws the initial structural geometry on the computer screen, then uses a combination of graphic and text inputs to: refine the structural geometry, specify information required for analysis such as panel load conditions, and define design variables and constraints for minimum-mass optimization. Composite panel design is an ideal application because the graphical user interface can: serve as a visual aid, eliminate the tedious aspects of text-based input, and eliminate many sources of input errors.

Lucas, S. H.↗
DOE OSTI2021

Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Validation of the single-event method for low-energy electron transport via stopping power calculations with $\mathrm{MCNP}$

Monte Carlo simulations of low-energy ( <50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. Here in this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Untangling Sources of Error in the Density-Functional Many-Body Expansion

The many-body expansion provides a framework for data-driven applications of electronic structure theory, including parametrization of classical force fields and machine learning. In this article, we demonstrate that its use significantly amplifies quadrature grid errors when modern density-functional approximations are employed. Standard grids that work well in conventional density-functional calculations result in runaway error accumulation when used with the many-body expansion. At the same time, delocalization error is also exacerbated, leading to exaggerated estimates of nonadditive n-body interactions. This is illustrated for anion–water clusters using the SCAN, r2SCAN, ωB97X-V and ωB97M-V functionals. By employing dense quadrature grids, the inherent self-interaction error is exposed, which can then be mitigated using a variety of other strategies.

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

Calibration, measurement, and characterization of soil moisture dynamics in a central Amazonian tropical forest

Soil moisture plays a key role in hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. Accurate soil moisture measurements in remote ecosystems such as the Amazon are difficult and limited because of logistical constraints. Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture and require calibration to convert the TDR's dielectric permittivity measurement (K a ) to volumetric water content (θ v ). In this study, our objectives were to develop a field-based calibration of TDR sensors in an old-growth upland forest in the central Amazon, to evaluate the performance of the calibration, and then to apply the calibration to determine the dynamics of soil moisture content within a 14.2 -m-deep vertical soil profile. Depth-specific TDR calibration using local soils in a controlled laboratory setting yielded a novel K a –θ v third-degree polynomial calibration. The sensors were later installed to their specific calibration depth in a 14.2-m pit. The widely used K a –θ v relationship (Topp model) underestimated the site-specific θ v by 22–42%, indicating significant error in the model when applied to these well-structured, clay-rich tropical forest soils. The calibrated wet- and dry-season θ v data showed a variety of depth and temporal variations highlighting the importance of soil textural differentiation, root uptake depths, as well as event to seasonal precipitation effects. Data such as these are greatly needed for improving our understanding of ecohydrological processes within tropical forests and for improving models of these systems in the face of changing environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

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

Heisenberg-limited Hamiltonian learning for interacting bosons

We develop a protocol for learning a class of interacting bosonic Hamiltonians from dynamics with Heisenberg-limited scaling. For Hamiltonians with an underlying bounded-degree graph structure, we can learn all parameters with root mean square error ϵ using ${\mathcal{O}}(1/\epsilon )$ total evolution time, which is independent of the system size, in a way that is robust against state-preparation and measurement error. In the protocol, we only use bosonic coherent states, beam splitters, phase shifters, and homodyne measurements, which are easy to implement on many experimental platforms. A key technique we develop is to apply random unitaries to enforce symmetry in the effective Hamiltonian, which may be of independent interest.

computer science↗