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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transient Stability Enhancement via a Scalable RL Method with VSG Parameter Tuning

This paper presents a reinforcement learning (RL)-driven strategy to improve the transient stability of power systems via tuning parameters of multiple virtual synchronous generators (VSGs). We proposed a scalable method to support RL training convergence probability and speed, even when a large number of contingencies are considered. The proposed scalable RL framework first decomposes the large number of contingencies into multiple groups and then conducts parallel training for each group, decreasing the state space and complexity of each training. Additionally, we propose a contingency grouping algorithm to streamline the RL action space and facilitate the training. The proposed method is validated across various standard test systems.

Huang, Xiaoge↗
DOE OSTI2024

Temperature-tuned ultrafast X-ray shutter using optics-on-a-chip

Typically modulation systems are incapable of performing synchronous modulation for high-energy radiation systems. A method and system for performing high-energy synchronous radiation modulating is described. The method includes providing an oscillatory diffractive element, with the oscillatory diffractive element capable of being oscillated over a range of angles. A radiation source provides radiation to the oscillatory diffractive element. An electrical signal is provided to electrodes that oscillate the oscillatory diffractive element to modulate the radiation. A temperature controller controls the temperature of the oscillatory diffractive element to tune the oscillatory motion of the oscillatory diffractive element.

Wang, Jin↗
DOE OSTI2022

Pressure-tuning of the electronic and magnetic properties of EuPt 2 Si 2

Using the element and orbital selectivity of x-ray absorption spectroscopy at Eu and Pt L 3 edges we investigate the effects of the lattice contraction, induced by temperature and external pressure, on the magnetic and electronic properties of the EuPt 2 Si 2 system. Our findings point to a clear relationship between the volume and the Eu valence in this material. From XANES experiments as a function of pressure we show that the Europium valence tends to stabilize at 3+ for pressures up to 27 GPa. The XMCD results demonstrate that pressure induced valence change of the Europium ion leads to a suppression of the magnetic ordering of the material. Finally, altogether our results provide direct evidence that Eu-4f /5d electronic hybridization effects underlie the mechanism that regulates the valence and magnetic ordering in this material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fluorine-Tuned Carbon-Based Nickel Single-Atom Catalysts for Scalable and Highly Efficient CO 2 Electrocatalytic Reduction

Electrocatalytic CO 2 reduction is garnering significant interest due to its potential applications in mitigating CO 2 and producing fuel. However, the scaling up of related catalysis is still hindered by several challenges, including the cost of the catalytic materials, low selectivity, small current densities to maintain desirable selectivity. In this study, Fluorine (F) atoms were introduced into an N-doped carbon-supported single nickel (Ni) atom catalyst via facile polymer-assisted pyrolysis. This method not only maintains the high atom utilization efficiency of Ni in a cost-effective and sustainable manner but also effectively manipulates the electronic structure of the active Ni-N 4 site through F doping. The catalyst has also been further optimized by controlling the F states, including convalent and semi-ionic states, by adjusting the fluorine sources involved. Consequently, this catalyst with unique structure exhibited comparable electrocatalytic performance for CO 2 -to-CO conversion, achieving a Faradaic efficiency (FE) of over 99% across a wide potential range and an exceptional CO evolution rate of 9.5 x 10 4 h -1 at -1.16 V vs reversible hydrogen electrode (RHE). It also delivered a practical current of 400 mA cm -2 while maintaining more than 95% CO FE. Experimental analysis combined with density functional theory (DFT) calculations have also shown that F-doping modifies the electron configuration at the central Ni-N 4 sites. In conclusion, this modification lowers the energy barrier for CO 2 activation, thereby facilitating the production of the crucial *COOH intermediate.

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

Intercalation as a versatile tool for fabrication, property tuning, and phase transitions in 2D materials

Recent advances in two-dimensional (2D) materials have led to the renewed interest in intercalation as a powerful fabrication and processing tool. Intercalation is an effective method of modifying the interlayer interactions, doping 2D materials, modifying their electronic structure or even converting them into starkly different new structures or phases. Herein, we discuss different methods of intercalation and provide a comprehensive review of various roles and applications of intercalation in next-generation energy storage, optoelectronics, thermoelectrics, catalysis, etc. The recent progress in intercalation effects on crystal structure and structural phase transitions, including the emergence of quantum phases are also reviewed.

2D materials↗
DOE OSTI2023

Tuning microwave losses in superconducting resonators

Abstract The performance of superconducting resonators, particularly cavities for particle accelerators and micro cavities and thin film resonators for quantum computations and photon detectors, has been improved substantially by recent material treatments and technological advances. As a result, the niobium cavities have reached the quality factors Q ∼ 10 11 at 1–2 GHz and 1.5 K and the breakdown radio-frequency (rf) fields H close to the dc superheating field of the Meissner state. These advances raise the questions of whether the state-of-the-art cavities are close to the fundamental limits, what these limits actually are, and to what extent the Q and H limits can be pushed by the materials nano structuring and impurity management. These issues are also relevant for many applications using high-Q thin film resonators, including single-photon detectors and quantum circuits. This topical review outlines basic physical mechanisms of the rf nonlinear surface impedance controlled by quasiparticles, dielectric losses and trapped vortices, as well as the dynamic field limit of the Meissner state. Sections cover methods of engineering an optimum quasiparticle density of states and superfluid density to reduce rf losses and kinetic inductance by pairbreaking mechanisms related to magnetic impurities, rf currents, and proximity-coupled metallic layers at the surface. A section focuses on mechanisms of residual surface resistance, which dominates rf losses at ultra low temperatures. Microwave losses of trapped vortices and their reduction by optimizing the concentration of impurities and pinning potential are also discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

FINETUNA: fine-tuning accelerated molecular simulations

Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
DOE OSTI2020

Tuning Fermi Levels in Intrinsic Antiferromagnetic Topological Insulators MnBi 2 Te 4 and MnBi 4 Te 7 by Defect Engineering and Chemical Doping

MnBi 2 Te 4 and MnBi 4 Te 7 are intrinsic antiferromagnetic topological insulators, offering a promising materials platform for realizing exotic topological quantum states. However, high densities of intrinsic defects in these materials not only cause bulk metallic conductivity, preventing the measurement of quantum transport in surface states, but may also affect magnetism and topological properties. In this paper, systematic density functional theory calculations reveal specific material chemistry and growth conditions that determine the defect formation and dopant incorporation in MnBi 2 Te 4 and MnBi 4 Te 7 . The large strain induced by the internal heterostructure promotes the formation of large-size-mismatched antisite defects and substitutional dopants. The results here show that the abundance of antisite defects is responsible for the observed n-type metallic conductivity. Additionally, a Te-rich growth condition is predicted to reduce the bulk free electron density, which is confirmed by experimental synthesis and transport measurements in MnBi 2 Te 4 . Furthermore, Na doping is proposed to be an effective acceptor dopant to pin the Fermi level within the bulk band gap to enable the observation of surface quantum transport. The defect engineering and doping strategies proposed here should stimulate further studies for improving synthesis and for manipulating magnetic and topological properties in MnBi 2 Te 4 , MnBi 4 Te 7 , and related magnetic topological insulators.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Tuning Spin-Orbit Torques Across the Phase Transition in VO 2 /NiFe Heterostructure

The emergence of spin-orbit torques as a promising approach to energy-efficient magnetic switching has generated large interest in material systems with easily and fully tunable spin-orbit torques. We report current-induced spin-orbit torques in VO 2 /NiFe heterostructures are investigated using spin-torque ferromagnetic resonance, where the VO 2 layer undergoes a prominent insulator-metal transition. A roughly twofold increase in the Gilbert damping parameter, α, with temperature is attributed to the change in the VO 2 /NiFe interface spin absorption across the VO 2 phase transition. More remarkably, a large modulation (±100%) and a sign change of the current-induced spin-orbit torque across the VO 2 phase transition suggest two competing spin-orbit torque generating mechanisms. The bulk spin Hall effect in metallic VO 2 , corroborated by the first-principles calculation of the spin Hall conductivity σ SH ≈ -10 4 ($\frac{\hbar}{e}$) Ω -1 m -1 , is verified as the main source of the spin-orbit torque in the metallic phase. The self-induced/anomalous torque in NiFe, with opposite sign and a similar magnitude to the bulk spin Hall effect in metallic VO 2 , can be the other competing mechanism that dominates as temperature decreases. For applications, the strong tunability of the torque strength and direction opens a new route to tailor spin-orbit torques of materials that undergo phase transitions for new device functionalities.

36 MATERIALS SCIENCE↗