Search NASA⌕ Search

SEARCH · Search NASA

Results for “Predictors”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fundamental microscopic properties as predictors of large-scale quantities of interest: Validation through grain boundary energy trends

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. Here, we propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of “synthetic materials”. As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Irradiated Brain Volume Within 12 Gy Is a Predictor for Radiation-Induced Changes After Stereotactic Radiosurgery in Patients With Unruptured Cerebral Arteriovenous Malformations

Our purpose was to determine whether the coverage of brain parenchyma within the 12 Gy radiosurgical volume (V12) correlates with the development of radiation-induced changes (RICs) in patients with unruptured cerebral arteriovenous malformations (AVM) after undergoing stereotactic radiosurgery (SRS).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Patterns and Predictors of Failure in Recurrent or Refractory Large B-Cell Lymphomas After Chimeric Antigen Receptor T-Cell Therapy

Chimeric antigen receptor T-cell (CAR T) therapy is capable of eliciting durable responses in patients with relapsed/refractory (R/R) lymphomas. However, most treated patients relapse. Patterns of failure after CAR T have not been previously characterized, and may provide insights into the mechanisms of resistance guiding future treatment strategies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Electrostatic Potential of Functional Cations as a Predictor of Hydroxide Diffusion Pathways in Nanoconfined Environments of Anion Exchange Membranes

Nanoconfined anion exchange membranes (AEMs) play a vital role in emerging electrochemical technologies. The ability to control dominant hydroxide diffusion pathways is an important goal in the design of nanoconfined AEMs. Such control can shorten hydroxide transport pathways between electrodes, reduce transport resistance, and enhance device performance. In this work, we propose an electrostatic potential (ESP) approach to explore the effect of the polymer electrolyte cation spacing on hydroxide diffusion pathways from a molecular perspective. By exploring cation ESP energy surfaces and validating outcomes through prior ab initio molecular dynamics simulations of nanoconfined AEMs, we find that we can achieve control over preferred hydroxide diffusion pathways by adjusting the cation spacing. The results presented in this work provide a unique and straightforward approach to predict preferential hydroxide diffusion pathways, enabling efficient design of highly conductive nanoconfined AEM materials for electrochemical technologies.

25 ENERGY STORAGE↗
DOE OSTI2021

The Oceanic Barrier Layer in the Eastern Indian Ocean as a Predictor for Rainfall Over Indonesia and Australia

Barrier layers in the tropics trap heat in a shallow and stable near-surface layer and limit entrainment of cooler water from below. Both processes act to increase sea surface temperature and enhance atmospheric convection. The high resolution fully coupled pre-industrial Energy Exascale Earth System Model version 0 (E3SMv0) is used to investigate the relationship between barrier layers in the eastern Indian Ocean during the wet season with local atmospheric convection and remote rainfall. A partial least squares regression reveals a significant relationship between Australasian rainfall and the barrier layer thickness (BLT) west of Sumatra, occurring one month earlier. Furthermore, the largest positive regression coefficients are over northern Australia. The region west of Sumatra is strategically located where the East-Asian monsoon moisture flows toward northern Australia. Thickening of the west Sumatra BLT intensifies evaporation and local convection and amplifies the moisture transported to Australia acting to increase the terrestrial rainfall.

58 GEOSCIENCES↗
DOE OSTI2023

Ion solvation as a predictor of lanthanide adsorption structures and energetics in alumina nanopores

Adsorption reactions at solid-water interfaces define elemental fate and transport and enable contaminant clean-up, water purification, and chemical separations. For nanoparticles and nanopores, nanoconfinement may lead to unexpected and hard-to-predict products and energetics of adsorption, compared to analogous unconfined surfaces. Here we use X-ray absorption fine structure spectroscopy and operando flow microcalorimetry to determine nanoconfinement effects on the energetics and local coordination environment of trivalent lanthanides adsorbed on Al 2 O 3 surfaces. We show that the nanoconfinement effects on adsorption become more pronounced as the hydration free energy, ΔG hydr , of a lanthanide decreases. Neodymium (Nd 3+ ) has the least exothermic ΔG hydr (-3336 kJ·mol -1 ) and forms mostly outer-sphere complexes on unconfined Al 2 O 3 surfaces but shifts to inner-sphere complexes within the 4 nm Al 2 O 3 pores. Lutetium (Lu 3+ ) has the most exothermic ΔG hydr (-3589 kJ·mol -1 ) and forms inner-sphere adsorption complexes regardless of whether Al 2 O 3 surfaces are nanoconfined. Importantly, the energetics of adsorption is exothermic in nanopores only, and becomes endothermic with increasing surface coverage. Changes to the energetics and products of adsorption in nanopores are ion-specific, even within chemically similar trivalent lanthanide series, and can be predicted by considering the hydration energies of adsorbing ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine learning methods for probabilistic locked-mode predictors in tokamak plasmas

A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbations, also known as error fields. This can lead to locking and then to disruptions. We leverage machine learning (ML) methods to predict the locking events. We use a coupled third-order nonlinear ordinary differential equation model to represent the interaction of the magnetic perturbation and the plasma rotation with the error field. This model is sufficient to describe qualitatively the locking and unlocking bifurcations. Here, we explore using ML algorithms with the simulation data and experimental data, focusing on the methods that can be used with sparse datasets. These methods lead to the possibility of the avoidance of locking in real-time operations. We describe the operational space in terms of two control parameters: the magnitude of the error field and the rotation frequency associated with the momentum source that maintains the plasma rotation. The outcomes are quan- tified by order parameters that completely characterize the state, whether locked or unlocked. We use unsupervised ML methods to classify locked/unlocked states and note the usefulness of a certain normalization of the order parameters. Three supervised ML classifiers are used in suite to estimate the probability of locking in the region of control parameter space with hysteresis, i.e., the set of control parameters for which both locked and unlocked states can exist. The results show that a neural network gives the best estimate of the locking probability. An analogy of the present locking model with the van der Waals equation of state is also provided.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Probabilistic locked mode predictor in the presence of a resistive wall and finite island saturation in tokamaks

We present a framework for estimating the probability of locking to an error field in a rotating tokamak plasma. This leverages machine learning methods trained on data from a mode-locking model, including an error field, resistive magnetohydrodynamics modeling of the plasma, a resistive wall, and an external vacuum region, leading to a fifth-order ordinary differential equation (ODE) system. It is an extension of the model without a resistive wall introduced by Akçay et al. [Phys. Plasmas 28, 082106 (2021)]. Tearing mode saturation by a finite island width is also modeled. We vary three pairs of control parameters in our studies: the momentum source plus either the error field, the tearing stability index, or the island saturation term. The order parameters are the time-asymptotic values of the five ODE variables. Normalization of them reduces the system to 2D and facilitates the classification into locked (L) or unlocked (U) states, as illustrated by Akçay et al., [Phys. Plasmas 28, 082106 (2021)]. This classification splits the control space into three regions: L̂, with only L states; Û, with only U states; and a hysteresis (hysteretic) region Ĥ, with both L and U states. In regions L̂ and Û, the cubic equation of torque balance yields one real root. Region Ĥ has three roots, allowing bifurcations between the L and U states. The classification of the ODE solutions into L/U is used to estimate the locking probability, conditional on the pair of the control parameters, using a neural network. We also explore estimating the locking probability for a sparse dataset, using a transfer learning method based on a dense model dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Charge-induced atomic strain as a predictor of structural phase transformation in rare-earth intermetallics

We present a descriptor based on charge-induced atomic strain in crystalline lattices for predicting structural phase transformations in rare-earth intermetallic compounds containing lanthanides and transition metals. The charge-induced local atomic strain was obtained from structural optimization of experimentally known crystalline phases using state of the art density-functional theory methods. The predictive power of the descriptor was evaluated on 𝑅⁢𝐸 2 ⁢In (𝑅𝐸 = rare earth) compounds, a class known for diverse phase transformations. We show that incorporating quantum-mechanical effects—such as local charge distribution, bonding, symmetry, and electronic structure—enhances the robustness of the descriptor. To gain further insight, we analyzed phononic and electronic behavior in Y 2 ⁢In and demonstrated that experimental phase transformations are captured only when atomic strain effects are included. The descriptor was further used to predict structural phase changes in (Y⁢b 1–𝑥 ⁢E⁢r 𝑥 ) 2 ⁢In and G⁡d 2 ⁡(I⁢n 1–𝑥⁢ A⁢l 𝑥 ), with predictions confirmed by x-ray powder diffraction. Here, while the current study is focused on lanthanide-based intermetallics, the underlying principles of the descriptor suggest potential applicability to other closely related classes of rare-earth intermetallics.

Density functional theory↗