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At least 397 records · Page 22

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DOE OSTI2022

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Here, based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4–B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.

36 MATERIALS SCIENCE↗

DOE OSTI2022

graphenv: a Python library for reinforcement learning on graph search spaces

Many important and challenging problems in combinatorial optimization (CO) can be expressed as graph search problems, in which graph vertices represent full or partial solutions and edges represent decisions that connect them. Graph structure not only introduces strong relational inductive biases for learning (Battaglia et al., 2018) - in this context, by providing a way to explicitly model the value of transitioning (along edges) between one search state (vertex) and the next - but lends itself to problems both with and without clearly defined algebraic structure. For example, classic CO problems on graphs such as the Traveling Salesman Problem (TSP) can be expressed as either pure graph search or integer programs. Other problems, however, such as molecular optimization, do no have concise algebraic formulations and yet are readily implemented as a graph search (V. et al., 2022; Zhou et al., 2019). Such "model-free" problems constitute a large fraction of modern reinforcement learning (RL) research owing to the fact that it is often much easier to write a forward simulation that expresses all of the state transitions and rewards, than to write down the precise mathematical expression of the full optimization problem. In the case of molecular optimization, for example, one can use domain knowledge alongside existing software libraries to model the effect of adding a single bond or atom to an existing but incomplete molecule, and let the RL algorithm build a model of how good a given decision is by "experiencing" the simulated environment many times through. In contrast, a model-based mathematical formulation that fully expresses all the chemical and physical constraints is intractable. In recent years, RL has emerged as an effective paradigm for optimizing searches over graphs and led to state-of-the-art heuristics for games like Go and chess, as well as for classical CO problems such as the TSP. This combination of graph search and RL, while powerful, requires non-trivial software to execute, especially when combining advanced state representations such as Graph Neural Networks (GNN) with scalable RL algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Power balance analysis at the L-H transition in JET-ILW NBI-heated deuterium plasmas

The understanding of the physics underlying the L-H transition has strong implications for ITER experimental reactor and demonstration power plant (DEMO). In many tokamaks, including JET, it has been observed that, at a particular plasma density, n e,min , the power necessary to access H-mode P L-H is minimum. In the present work, L-H transitions of JET deuterium plasmas heated by neutral beam injection (NBI) are studied for the first time by means of a power balance analysis to characterize the main contributions in the transition, through integrated transport modelling. In the pulses analysed, we do observe a minimum of the L-H power threshold in density, indicating the presence of density branches and of n e,min . Electron and ion heat fluxes at the transition are estimated separately. The electron/ion equipartition power results in favour of the ions, as shown by QuaLiKiz quasilinear gyrokinetic simulations, which predict a larger ion transport that causes T e > T i . The resulting edge ion heat flux also shows a clear change of slope below n e,min , similarly to ASDEX-Upgrade (AUG) NBI pulses (Ryter et al 2014 Nucl. Fusion 54 083003). JET NBI data are compared to radio-frequency heated AUG and Alcator C-mod pulses (Schmidtmayr et al 2018 Nucl. Fusion 58 056003), showing a different trend of the power, coupled to ions at the L-H transition with respect to the linearity observed in the radio-frequency heated plasmas. Finally, the presence of n e,min and the role of the ion heat flux is discussed in the paper, although it seems it is not possible to explain the presence of a P L-H minimum in density by a critical ion heat flux and by the equipartition power for the JET NBI-heated plasmas analysed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Constitutive modeling of δ-phase zircaloy hydride based on strain rate dependent nanoindentation and nano-scale impact dataset

In this work, a constitutive model for zircaloy-4 embedded with δ-phase hydride rim/blister structures (δ-phase zircaloy hydride) is presented. Nanoindentation and nano-scale impact experiments were performed on the δ-hydride blisters as a function of temperature in order to obtain strain rate dependent location specific constitutive response. A power law viscoplastic constitutive model was chosen to represent the strain rate dependent behavior. The strength coefficient and the strain hardening exponent in the power law relation were found to decrease with increase in temperature indicating softening behavior at higher temperatures. The strain rate exponent remained constant with increase in temperature indicating that the rate dependent ductility does not change significantly at higher temperatures. A finite element method based model was created to analyze the failure behavior and the ductile to brittle transition in zircaloy hydride. The model predicted that fracture strength of δ-phase zircaloy hydride decreases linearly with increase in temperature.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Orbital correlations in ultrathin films of late transition metals

We develop a two-orbital Hubbard model of electron correlations in ultrathin (111)-oriented fcc films of late transition metals such as Co and Ni. Our model indicates that the Mott-Hund’s interaction results in ferromagnetic nearest-neighbor orbital correlations. Frustration associated with the mismatch between orbital and crystal symmetries prevents orbital ordering, resulting in the orbital liquid state. This state can be manifested in phenomena involving spin-orbit coupling, such as magnetic anisotropy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Water resource recovery modelling 2021 (WRRmod2021 conference)

Our society is transitioning fast into the digital age, spurred by development of cheap and new sensing technology, breakthroughs in computing, and development of efficient algorithms for optimization. This transition is also visible in the field of wastewater treatment and is driving new model developments, especially by exploiting the large data sets available with many utilities. Not surprisingly, WRRmod2021 had featured a strong session on ‘data-driven models and digitalization’ focused on this hot topic. At the same time, engineering practice calls for more robust models for performance evaluation and optimization of both conventional facilities and innovative processes. As a result, the WRRmod2021 program also exhibited sessions on modelling of new process units (e.g., aerobic granular sludge), modelling of the nitrogen cycle, and integrated/plant-wide modelling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Designing polar textures with ultrafast neuromorphic features from atomistic simulations

This review summarizes recent works, all using a specific atomistic approach, that predict and explain the occurrence of key features for neuromorphic computing in three archetypical dipolar materials, when they are subject to THz excitations. The main ideas behind such atomistic approach are provided, and illustration of model relaxor ferroelectrics, antiferroelectrics, and normal ferroelectrics are given, highlighting the important potential of polar materials as candidates for neuromorphic computing. Some peculiar emphases are made in this Review, such as the connection between neuromorphic features and percolation theory, local minima in energy path, topological transitions and/or anharmonic oscillator model, depending on the material under investigation. By considering three different and main polar material families, this work provides a complete and innovative toolbox for designing polar-based neuromorphic systems.

THz pulses↗

DOE OSTI2020

Understanding the metal-to-insulator transition in La1−xSrxCoO3−δ and its applications for neuromorphic computing

Abstract Transition metal oxides that exhibit a metal-to-insulator transition (MIT) as a function of oxygen vacancy concentration are promising systems to realize energy-efficient platforms for neuromorphic computing. However, the current lack of understanding of the microscopic mechanism driving the MIT hinders the realization of effective and stable devices. Here we investigate defective cobaltites and we unravel the structural, electronic, and magnetic changes responsible for the MIT when oxygen vacancies are introduced in the material. We show that, contrary to accepted views, cooperative structural distortions instead of local bonding changes are responsible for the MIT, and we describe the subtle interdependence of structural and magnetic transitions. Finally, we present a model, based on first principles, to predict the required electric bias to drive the transition, showing good agreement with available measurements and providing a paradigm to establish design rules for low-energy cost devices.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Can we rely on drought–ending “miracles” in the Colorado River Basin?

Unexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the “Miracle May” of 2015. Although these events are often termed as “drought-busting” or “miracle events” by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard-to-predict, ultra-high precipitation events occurring during the late-snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry-to-wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. Finally, in view of the potentially wetter but less-snowy climate in the basin, the need for future research to more quantitatively assess these “miracle events” is emphasized.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Scenarios for Future Energy Systems

Energy systems in the U.S. and globally have continuously changed and expanded over the past 200 years, from animal and biomass based energy to electricity and petroleum for heat, light, transportation, and industries of all kinds. The most recent two decades have experienced a significant increase in the use of natural gas, solar, and wind energy, as well as energy efficiency, due to both technology breakthroughs and public policy. With history as a guide, we can expect on-going transformation of our energy system in future decades, including increased electrification and advanced energy technologies, ideally helping meet societal goals of reducing pollution, improving quality of human life, and preserving ecosystems. Scenario modeling is an important tool for planning energy system transformations toward specific goals - such as net-zero greenhouse gas emissions, 100% renewable energy, or energy security - particularly when there are multiple options, conflicting objectives, and significant unknowns about a path forward. This presentation will review the history of energy transitions and the future policy and societal objectives on energy. Then it will summarize multiple scenario studies of future energy systems, with a focus on "100% studies" and the challenges to meeting those objectives. It will also outline of a few of the technologies being researched now that could advance the ongoing energy transition.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY,↗

DOE OSTI2025

A Theory of Ultrafast Charge Transfer Relaxation with Non-Innocent Solvent Molecules

In this work we revisit the photodynamics of tetracyanoethylene-hexamethylbenzene (TCNE- HMB), the molecular complex studied by Hochstrasser et al. [J. Chem. Phys. 100, 4797–4810, 1994] that has long challenged the applicability of Marcus’s theory of elec- tron transfer for predicting photochemical reactions. Using a novel black-box electronic structure algorithm (time-dependent density functional theory with one double, TD- DFT-1D) to efficiently run molecular dynamics that can treat charge recombination, we run ab initio surface hopping molecular dynamics and confirm that, for a polar solvent, charge recombination rates can be incredibly fast (indeed faster than the sol- vent relaxation time); for non-polar solvents, the rate is much slower. We demonstrate that, although Marcus theory cannot be directly applied, these nonequilibrium (and sometimes incredibly fast) photoexcited dynamics can be effectively explained within a two-state model without any evidence of a transition through a conical intersection. Most importantly, for this paradigmatic model system, we are able to identify two nuclear coordinates of interest (rather than the single coordinate predicted by Marcus or a full set of internal quantum modes studied by Bixon and Jortner): the solvent relaxation in the first shell (that strongly modulates the energies of the charge trans- fer state and differentiates time scales for relaxation) and a nuclear displacement in the TCNE-HMB complex arising from a handful of vibrations that induces non-Born Oppenheimer motion and eventually facilitates an abrupt electronic transition to the ground state. Altogether, these findings suggest a tractable generalization of Marcus theory for future simulations of photochemistry with non-innocent solvent environ- ments in the spirit of a Hamiltonian suggested by Stuchebrukhov (J. Chem. Phys. 107, 3821, 1997).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Genuine k -partite correlations and entanglement in the ground state of the Dicke model for interacting qubits

Here, in this work, we calculate and study correlations of the Dicke model in the presence of qubit–qubit interaction. Whereas the analysis of correlations among its subsystems is essential for the understanding of corresponding critical phenomena and for performing quantum information tasks, the majority of correlation measures are restricted to bipartitions due to the inherent challenges associated with handling multiple partitions. To circunvent this we employ the calculation of Genuine Multipartite Correlations (GMC) based on the invariance of our model under particle permutation. We then quantify the correlations within each subpart of the system, as well as the percentage contribution of each GMC of order $k$, highlighting the many-body behaviors for different regimes of parameters. Additionally, we show that GMC signal both first- and second-order quantum phase transitions present in the model. Furthermore, as GMC encompasses both classical and quantum correlations, we employ Quantum Fisher Information (QFI) to detect genuine multipartite entanglement. Ultimately, we map the Dicke model with interacting qubits to spin in solids interacting with a quantum field of magnons, thus demonstrating a potential experimental realization of this model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Global climate mode resonance due to rapidly intensifying El Niño-Southern Oscillation

The El Niño-Southern Oscillation (ENSO) influences climate variability globally, encompassing various other modes of variability, and thus represents a key predictable climate signal on seasonal timescales. Yet, its response to greenhouse warming remains uncertain, with models projecting a range of outcomes. Here, we demonstrate that in response to warming, a state-of-the-art high-resolution climate model simulates a rapid transition from a moderate-amplitude irregular regime, as observed in the current climate, to a highly regular oscillation with intensifying amplitude. This behaviour can be attributed to increasing air-sea feedbacks, which approach criticality in the second half of this century, and growing atmospheric noise. As ENSO intensifies in this model, it synchronizes with other prominent climate modes, such as the North Atlantic Oscillation and the Indian Ocean Dipole, thereby imprinting its regular, predictable variability on them. If realized, this global climate mode resonance would have wide-ranging whiplash impacts on regional hydroclimates.

Atmospheric dynamics↗

DOE OSTI2021

Neutron-unbound states in 31 Ne

Background: The Island of Inversion near the N = 20 shell gap is home to nuclei with reordered single-particle energy levels compared to the spherical shell model. Studies of 31 Ne have revealed that its ground state has a halo component, characterized by a valence neutron orbiting a deformed 30 Ne core. This lightly-bound nucleus with a separation energy of only S n = 170 keV is expected to have excited states that are neutron unbound. Purpose: The purpose of this experiment was to investigate the low-lying excited states in 31 Ne that decay by the emission of a single neutron. Methods: An 89 MeV/u 33 Mg beam impinged on a segmented Be reaction target. Neutron unbound states in 31 Ne were populated via a two-proton knockout reaction. The 30 Ne fragment and associated neutron from the decay of 31 Ne were detected by the MoNA-LISA-Sweeper experimental setup at the National Superconducting Cyclotron Laboratory. Invariant mass spectroscopy was used to reconstruct the two-body decay energy ( 30 Ne + n). Results: The two-body decay energy spectrum exhibits two features: a low-lying peak at 0.30 ± 0.17 MeV and a broad enhancement at 1.50 ± 0.33 MeV, each fit with an energy-dependent asymmetric Breit-Wigner line shape representing a resonance in the continuum. Accompanying shell model calculations using the FSU interaction within NuShellX, combined with cross-section cal culations using the eikonal reaction theory, indicate that these peaks in the decay energy spectrum are caused by multiple resonant states in 31 Ne. Conclusions: In this study, excited states in 31 Ne were observed for the first time. Transitions from calculated shell model final states in 31 Ne to bound states in 30 Ne are in good agreement with the measured decay energy spectrum. Cross-section calculations for the two-proton knockout populating 31 Ne states as well as spectroscopic factors pertaining to the decay of 31 Ne into 30 Ne are used to examine the results within the context of the shell model expectations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Machine Learning and Economic Models to Enable Risk-Informed Condition Based Maintenance of a Nuclear Plant Asset

The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Order-disorder versus displacive transitions in Jahn-Teller active layered materials

Large anharmonic vibrations often play a crucial role in dynamically stabilizing crystalline phases whose structures are unstable at low temperature. Although the average structure of such phases can be measured through diffraction experiments, their local structure remains a challenge to characterize and understand. Dynamically stabilized phases are often classified as order/disorder or displacive based on the qualitative nature of their local structure. A robust understanding of how chemistry determines this distinction in behavior, however, is lacking. This article presents a parametric study of an anharmonic vibrational model that describes the transition from a cooperative to a noncooperative Jahn-Teller distortion in layered oxides—a class of materials widely used in Li-ion and Na-ion batteries. The results illustrate how the shape of the energy landscape determines the extent to which the high-temperature phase has order/disorder vs displacive character. In this work, we find that the nature of the high-temperature phase is determined by a competition between the strength of the elastic coupling between Jahn-Teller distortions at nearby sites and the energy scale driving the Jahn-Teller distortion. A comparison of the model to energy landscapes calculated with density-functional theory suggests that the high-temperature phases of the Jahn-Teller active layered compounds LiNiO 2 , NaNiO 2 , LiMnO 2 , and NaMnO 2 exhibit order/disorder character.

36 MATERIALS SCIENCE↗

DOE OSTI2026

EV Shuttle Bus Pilot

The EV Shuttle Bus Pilot dataset contains data and analysis from Hocking-Athens-Perry Community Action's demonstration of an electric bus on routes of their rural Athens Public Transit system. The vehicle used in the demonstration was a Ford E-450 cutaway equipped with an electric drivetrain, a 127-kWh battery system by Motiv Power Systems, and a cabin upfit by Turtle Top. Data gathered include route assignments, running time and distance, fuel economy, and charge cycles. A comparison of the vehicle's observed duty cycle with duty cycle modeling from other rural transit fleets in the National Transit Database is included to help better understand the rural adoption potential for this fleet technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Numerical study of multi-component flow and mixing in a scaled fission product venting system

Numerical simulation of isothermal multi-component gas flow was conducted to study fluid flow and mixing in a scaled low-pressure, low-temperature test facility for the fission product venting system (FPVS) of a gas cooled fast reactor (GFR). The geometry of FPVS test facility was an open loop including gradual expansion coupling and two 90°pipe elbows. First, the validation of large eddy simulation (LES) wall-adapting local eddy-viscosity (WALE) turbulent model was performed for transitional flow in a circular pipe with gradual expansion. The reactingFOAM solver in OpenFOAM v8 was employed. By introducing appropriate turbulence disturbance at the flow inlet, such as turbulence intensity, the numerical results showed good agreement in (1) the velocity profile downstream of the pipe expansion measured by particle image velocimetry (PIV) in this study, and (2) the reattachment length reported in the literature. Using this validated model, the numerical simulation of flow and component mixing was performed for the FPVS with an inlet flowrate corresponding to a Reynolds number of 2,400 to investigate the flow behavior and component mixing. Standard deviation of the mass fraction of component, referred to as absolute mixing index (AMI), was calculated to quantify the mixing of components, showing that the component mixing length determined by AMI is related to the reattachment length downstream of the expansion. Additionally, this study was able to identify the best location in the FPVS test facility to measure the velocity and concentration profies where multicomponent flow is well mixed.

15 GEOTHERMAL ENERGY↗