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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

First principles study of the electronic structure of the Ni 2 MnIn / InAs and Ti 2 MnIn / InSb interfaces

We present a first principles study of the electronic and magnetic properties of epitaxial interfaces between the Heusler compounds, Ti 2 MnIn and Ni 2 MnIn, and the III-V semiconductors, InSb and InAs, respectively. We use density functional theory (DFT) with a machine-learned Hubbard U correction determined by Bayesian optimization. Here, we evaluate these interfaces for prospective applications in Majorana-based quantum computing and spintronics. In both interfaces, states from the Heusler penetrate into the gap of the semiconductor, decaying within a few atomic layers. The magnetic interactions at the interface are weak and local in space and energy. Magnetic moments of less than 0.1 µB are induced in the two atomic layers closest to the interface. The induced spin polarization around the Fermi level of the semiconductor decays within a few atomic layers. The decisive factor for the induced spin polarization around the Fermi level of the semiconductor is the spin polarization around the Fermi level in the Heusler, rather than the overall magnetic moment. As a result, the ferrimagnetic narrow-gap semiconductor Ti 2 MnIn induces a more significant spin polarization in the InSb than the ferromagnetic metal Ni 2 MnIn induces in the InAs. This is explained by the position of the transition metal d states in the Heusler with respect to the Fermi level. Based on our results, these interfaces are unlikely to be useful for Majorana devices but could be of interest for spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Connection between the physicochemical characteristics of amorphous carbon thin films and their electrochemical properties

Connecting a material's surface chemistry with its electrocatalytic performance is one of the major questions in analytical electrochemistry. This is especially important in many sensor applications where analytes from complex media need to be measured. Unfortunately, today this connection is still largely missing except perhaps for the most simple ideal model systems. Here we present an approach that can be used to obtain insights about this missing connection and apply it to the case of carbon nanomaterials. In this paper we show that by combining advanced computational techniques augmented by machine learning methods with x-ray absorption spectroscopy (XAS) and electrochemical measurements, it is possible to obtain a deeper understanding of the correlation between local surface chemistry and electrochemical performance. As a test case we show how by computationally assessing the growth of amorphous carbon (a-C) thin films at the atomic level, we can create computational structural motifs that may in turn be used to deconvolute the XAS data from the real samples resulting in local chemical information. Then, by carrying out electrochemical measurements on the same samples from which x-ray spectra were measured and that were further characterized computationally, it is possible to gain insight into the interplay between the local surface chemistry and electrochemical performance. To demonstrate this methodology, we proceed as follows: after assessing the basic electrochemical properties of a-C films, we investigate the effect of short HNO 3 treatment on the sensitivity of these electrodes towards an inner sphere redox probe dopamine to gain knowledge about the influence of altered surface chemistry to observed electrochemical performance. Overall, these results pave the way towards a more general assessment of electrocatalysis in different systems and provide the first steps towards data driven tailoring of electrode surfaces to gain optimal performance in a given application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗
DOE OSTI2023

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models

We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-source machine learning library, and PySCF, an ab initio computational chemistry program that provides simple and customized tools for developing quantum chemistry codes. It supports the DeePHF and DeePKS methods. In addition to explaining the details in the methodology and the software, we also provide an example of developing a chemically accurate model for water clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics

Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross section, tandem mass spectra) against reference data obtained from measurements of authentic chemical standards. The breadth of annotatable compounds using this approach is necessarily limited by availability of authentic standards, analytical throughput, and resolving power of the separations that underly the measurements. The maturation of computational methods, both theory-driven and artificial intelligence/machine learning-based, for prediction of various molecular properties relevant to multidimensional mass spectrometry measurements has opened the door to a new “reference-free” paradigm of compound annotation. Through augmenting existing reference data for molecular properties with computational predictions, the universe of identifiable chemical species can be expanded significantly beyond its current limits. An unexplored aspect of this novel approach is understanding how to gauge confidence in resulting annotations, especially as the compound search space is expanded. Intuitively, the confidence of a compound annotation is related to the inherent discriminatory power of the molecular properties used for identification, as well as the precision with which the properties are measured or predicted. In this work, we characterize this relationship between measurement precision and identification probability in a systematic and quantitative fashion for a defined region of chemical space that includes organic small molecule metabolites. Importantly, this work establishes a framework for conducting metabolite identification probability analysis that enables others to quantify this relationship for their own compounds and properties of interest.

Metabolite Identification↗
DOE OSTI2022

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. Here we reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.

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

Capturing dynamical correlations using implicit neural representations

Abstract Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S ( Q , ω ), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La 2 NiO 4 , showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Parametric matrix models

We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.

Computational science↗
DOE OSTI2024

Plutonium oxide melt structure and covalency

Advances in nuclear power reactors include the use of mixed oxide fuel, containing uranium and plutonium oxides. The high-temperature behaviour and structure of PuO 2-x above 1,800 K remain largely unexplored, and these conditions must be considered for reactor design and planning for the mitigation of severe accidents. Here, in this study, we measure the atomic structure of PuO 2-x through the melting transition up to 3,000 +/- 50 K using X-ray scattering of aerodynamically levitated and laser-beam-heated samples, with O/Pu ranging from 1.57 to 1.76. Liquid structural models consistent with the X-ray data are developed using machine-learned interatomic potentials and density functional theory. Molten PuO 1.76 contains some degree of covalent Pu-O bonding, signalled by the degeneracy of Pu 5f and O 2p orbitals. The liquid is isomorphous with molten CeO 1.75 , demonstrating the latter as a non-radioactive, non-toxic, structural surrogate when differences in the oxidation potentials of Pu and Ce are accounted for. These characterizations provide essential constraints for modelling pertinent to reactor safety design. The molten structure of plutonium oxide-a component of mixed oxide nuclear fuels-is measured, showing some degree of covalent bonding. Its atomic structure is similar to that of cerium oxide, which could be a non-radioactive structural surrogate.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Toward first principles-based simulations of dense hydrogen

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Infrared spectral signatures of interfacial water at TiO 2 –electrolyte interfaces from deep potential molecular dynamics

Vibrational spectroscopy is a powerful tool for probing water at oxide–electrolyte interfaces, but its molecular interpretation can be challenging. Here, we employ deep potential long-range molecular dynamics simulations with layer-resolved spectral analysis to investigate the microscopic origins of the infrared (IR) response of water at the interface with anatase TiO 2 (101), a prototypical oxide surface. The calculated interfacial spectra exhibit characteristic modifications compared to bulk water IR spectra, including enhanced intensities, a red shifted and broadened stretching band, and a higher-frequency shoulder, in qualitative agreement with experiments. Spectral decomposition shows that these signatures originate mainly from the first interfacial water layer, dominated by surface-bound H 2 O at Ti 5C sites, with secondary contributions from the second layer. A moderate salt concentration (0.4 M NaCl) leaves both the interfacial structure and the spectra essentially unchanged, while tuning the pH strongly modulates the spectral intensity. We establish a scaling relation linking the spectral intensity to the surface water dissociation fraction and the dipole moment, both governed by interfacial electric fields. These findings provide a microscopic framework for interpreting IR spectra of oxide–electrolyte interfaces.

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

Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Ice Ih, the common form of ice in the biosphere, contains proton disorder. Its proton-ordered counterpart, ice XI, is thermodynamically stable below 72 K. However, the formation of ice XI is kinetically hindered, and experimentally it is obtained by doping with KOH. Doping creates ionic defects that promote the migration of protons and the associated change in proton configuration. In this article, we mimic the effect of doping with a bias potential that enhances the formation of ionic defects in molecular dynamics simulations. The recombination of the ions thus formed proceeds through fast migration of the hydroxide along hydrogen bond loops, providing a physical and expedite way to change the proton configuration. Here, a key ingredient of this approach is a machine learning potential trained with density functional theory data and capable of modelling molecular dissociation. We exemplify the usefulness of this idea by studying the order-disorder transition using an appropriate order parameter that distinguishes the proton environments in ice Ih and XI. We calculate the changes in free energy, enthalpy, and entropy associated with the transition. Our estimated entropy agrees with experiment within the error bars of the calculation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Coarse-grain cluster analysis of tensors with application to climate biome identification

A tensor provides a concise way to codify the interdependence of complex data. Treating a tensor as a d-way array, each entry records the interaction between the different indices. Clustering provides a way to parse the complexity of the data into more readily understandable information. Clustering methods are heavily dependent on the algorithm of choice, as well as the chosen hyperparameters of the algorithm. However, their sensitivity to data scales is largely unknown. In this work, we apply the discrete wavelet transform to analyze the effects of coarse-graining on clustering tensor data. We are particularly interested in understanding how scale affects clustering of the Earth's climate system. The discrete wavelet transform allows classification of the Earth's climate across a multitude of spatial-temporal scales. The discrete wavelet transform is used to produce an ensemble of classification estimates, as opposed to a single classification. Each element of the ensemble is a clustering at a different spatial-temporal scale. Information theoretic approaches are used to identify important scale lengths in clustering the L15 Climate Datset. We also discover a sub-collection of the ensemble that spans the majority of the variance observed, allowing for efficient consensus clustering techniques that can be used to identify climate biomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Deep neural network for the dielectric response of insulators

In this work, we introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the deep potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab initio simulation. The scheme is nonperturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. Finally, we also provide extensive simulation results to demonstrate the validity of the proposed approach.

42 ENGINEERING↗
DOE OSTI2023

DOE STTR Phase I Final Report Report: Machine-learning Based Prediction of Thermal Limits for Conjugated Organic Materials

The SCANN-DT Phase I STTR project led by NLM Photonics and partnering with the National Renewable Energy Laboratory (NREL) sought to apply machine learning techniques based on graph neural networks (GNNs) towards the prediction of decomposition temperatures (Td) of organic semiconductors, based on prior work on bond dissociation energy (BDE) prediction as implemented in NREL’s ALFABET prediction tool. Using a curated set of experimental decomposition energies, the project examined GNN-based, classical quantitative structure-property relationship (QSPR) based on DFT calculations, and combinations of both methods to predict Td. While the best MAEs in Td achieved were near 40°C, below project targets, the project led to improvements in the ALFABET model, improvements in cloud-based implementations of NWChem software, and a substantial dataset of calculations on medium-sized conjugated organic molecules.

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

Management and Operation of the Lawrence Livermore National Laboratory (Final Report)

Optimize existing characterization and control methods by developing methods for rapid synthesis of multi-qubit control for error mitigation and complex gate design using optimal control theory. The Subcontractor shall develop Hamiltonian learning methods that can be used for characterizing super-conducting quantum devices, in particular develop strategies for effective probing of such devices.

97 MATHEMATICS AND COMPUTING↗