Dietary modulation of gut microbiota affects susceptibility to drug-induced liver injury
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Pan, Han [State Key Laboratory of Microbial Metabo↗
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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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Roadmap on data-centric materials science
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
36 MATERIALS SCIENCE↗
Enhancing nitrogen fixation efficiency in glow-like discharge by reducing cathode-fall voltage
In plasma nitrogen fixation devices, discharge electrodes are crucial yet susceptible to oxidation and corrosion due to plasma’s high temperatures and oxygen content, which could alter discharge modes. This research evaluates the impact of different electrode materials, including iron, chromium, nickel, copper, and 304 stainless steel, on nitrogen fixation efficiency in glow-like discharges driven by high-voltage DC power. Notably, iron and 304 stainless steel cathodes undergo a mode transition at increased currents, evident from plasma color shifts and significant voltage reductions. Fourier transform infrared spectroscopy analyses reveal that such mode changes minimally affect nitrogen oxide production rates, leading to a notable decrease in energy consumption for nitrogen fixation by up to 40%. OES and SEM-EDS measurements suggest that iron oxide, with its higher secondary electron emission, replaces metal as the cathode material, facilitating mode transitions and maintaining discharge current at lower voltages. Further, this voltage change is largely attributed to the cathode voltage drop, highlighting the minimal role of the cathode fall region in NO x synthesis. These findings underscore the potential for improving plasma nitrogen fixation energy efficiency by choosing suitable cathode materials to lower the cathode-fall voltage.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
New physics search at the CEPC: a general perspective
A next generation, high-intensity electron-positron collider “Higgs factory”, such as the Circular Electron-Positron Collider (CEPC), is among the highest priority for the global high energy collider physics community. The CEPC can provide unprecedented opportunities for making fundamental discoveries and providing decisive insights in the quest for a “New Standard Model (SM)” of nature’s fundamental interactions. The CEPC could: 1) Identify the origin of matter, especially the mechanism related to the first-order phase transition in the early Universe, which could produce a detectable gravitational wave signal. 2) Discover dark matter, particularly dark matter particles with a mass between one tenth and 100 times the proton mass. 3) Observe an array of new physics smoking guns, with sensitivities orders of magnitude better than those of existing facilities. The SM of Particle Physics is a triumph of the past half a century, as it predicts and interprets almost all the phenomena observed in experiments from the highest energies with colliders to low energy “tabletop” studies. On the other hand, deep mysteries exist concerning the most fundamental interactions of matter and the space-time fabric of the Universe, including the nature of dark matter, the origin of “visible” matter, the vast hierarchy of elementary particle masses, the quantum nature of gravity, and the mechanism of inflation. These mysteries challenge us to look for “new physics” beyond the SM and General Relativity. Indeed, physicists believe that the SM is simply a low-energy effective theory that reflects aspects of the more profound theory that answers the aforementioned mysteries. Uncovering this “New SM”, the profound theory who supports the SM is the primary mission for particle physics in the post-Higgs boson era.
Ai 艾, Xiaocong 小聪 [Zhengzhou University (China); e↗
Autoregressive long-horizon prediction of plasma edge dynamics *
Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data for the KSTAR tokamak, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1–100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.
autoregressive deep learning↗
A large interlaboratory electron diffraction study of monolayer graphene
Standardisation of data collection and analysis is essential to enable commercialisation of 2D materials in a wide range of technologies. Selected area electron diffraction (SAED) in the transmission electron microscope (TEM) is one of the key methods for distinguishing monolayer from bilayer and few-layer graphene by comparing the 1st and 2nd order diffraction spot intensities. Yet there are many factors that can affect the reliability of data collection and interpretation, causing the measurement of monolayer samples to deviate from the literature boundary condition of $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1 for monolayer graphene (1LG). Here we present the results of a large interlaboratory SAED comparison study, where 15 international laboratories measured and analysed nominally identical samples of chemical vapour deposited graphene. Large variations were observed in the measured ratios of diffraction spot intensities, with the largest variance associated with poor quality SAED data resulting from inadequate specimen handling and storage. To inform the reliable determination of monolayer thickness from SAED patterns we provide a description of best practice for specimen handling, TEM operation, data collection and analysis. This work was undertaken within VAMAS Technical Working Area 41: Graphene and related 2D materials—Project 9, the results of which have been directly incorporated into ISO/TS 21356–2 for the characterisation of graphene sheets. We find that when this methodology is followed, 1LG can be distinguished from bilayer or thicker material with high confidence where analysis of a single SAED pattern gives $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1.2, even in the absence of precise specimen tilting.
2D materials↗
Review of solar-enabled desalination and implications for zero-liquid-discharge applications
Abstract The production of freshwater from desalinating abundant saline water on the planet is increasingly considered a climate change adaptation measure. Yet, there are challenges associated with the high cost, intensive energy demand, and environmental implications of desalination. Effective integration of solar energy generation and freshwater production can address both issues. This review article highlights recent key advances in such integration achieved in a joint-research university-national laboratory partnership under the auspices of the United States Department of Energy and parallel efforts worldwide. First, an overview of current and emerging desalination technologies and associated pretreatment, brine treatment, and valorization technologies that together can result in zero-liquid-discharge systems is presented, and their technological readiness levels are evaluated. Then, advanced modeling techniques and new software platforms that enable optimization of solar-desalination applications with the dual objective of cost and environmental impact minimization are discussed.
14 SOLAR ENERGY↗
MATEY: multiscale adaptive transformer models for spatiotemporal physical systems
Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two novel adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavior to uniform patch refinement, while the other offers better computational efficiency. Moreover, we present a set of spatiotemporal attention schemes, where the temporal or axial spatial dimensions are decoupled, to evaluate their baseline computational and data efficiencies and to determine whether adaptive tokenization can improve this performance. We assess the performance of the proposed multiscale adaptive model, MATEY, in a sequence of experiments. Compared to a full spatiotemporal attention scheme or a scheme that decouples only the temporal dimension, we find that fully decoupled axial attention is less efficient and expressive, requiring more training time and model parameters to achieve the same accuracy. The experiments on the adaptive tokenization schemes show that, compared to a uniformly refined model, the proposed schemes achieve comparable or improved accuracy at a much lower cost in the tested two-dimensional settings. While the asymptotic analysis suggests the potential for favorable scaling, empirical validation at substantially longer sequence lengths remains to be performed in future work. Finally, we demonstrate in two fine-tuning tasks featuring different physics that models pretrained on PDEBench data outperform the ones trained from scratch, especially in the low data regime with frozen attention.
adaptive tokenization↗
Nonnegligible cascading impacts of global urban expansion on net primary productivity
Accelerated global urban expansion not only directly occupies surrounding ecosystems, but also induces cascading losses of natural vegetation elsewhere through cropland displacement. Yet, how such effects alter the net primary productivity (NPP) worldwide remains unclear. Here, we quantified the direct and cascading impacts of global urban expansion on terrestrial NPP from 1992 to 2020 and projected the impacts under the shared socioeconomic pathways framework by 2100. We found that global urban expansion caused a cascading loss of 29.2 to 63.9 Tg C/year of terrestrial NPP in the historical period (1992–2020), accounting for 13–29% of the total direct NPP loss. Instead, our projections indicate that during 2020–2100, mainly due to the increased relocation of displaced croplands to low-productive ecosystems, the cascading impacts gradually change from negative to positive, leading to a net NPP increase. Such an increase may offset up to 7% of the total direct NPP loss, better balancing crop compensation with NPP maintenance. Our findings highlight the unexpected large cascading impacts of urban expansion on the carbon cycle and stress the importance of regulating land transitions to curtail land-use emissions.
54 ENVIRONMENTAL SCIENCES↗
Momentum-Resolved Direct Observation of Chiral Phonons in Elemental Tellurium
Chiral phonons carry finite phonon angular momentum, yet their momentum-resolved behavior in acoustic branches remains largely unexplored. In this work, we report the direct detection of chiral acoustic phonons in elemental Te. Through a combination of high-energy-resolution inelastic x-ray scattering experiments and atomistic modeling, we confirm that longitudinal acoustic modes develop pronounced circular atomic motion by mixing with transverse modes. Our results establish a direct link between unexpected scattering intensity in nominally forbidden geometries and circular phonons, offering a framework for characterizing momentum-resolved phonon chirality and highlighting the emergence of intrinsic phonon angular momentum in acoustic branches.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Fault-Tolerant Operation of Bosonic Qubits with Discrete-Variable Ancillae
Fault-tolerant quantum computation with bosonic qubits often necessitates the use of noisy discrete-variable ancillae. In this work, we establish a comprehensive and practical fault-tolerance framework for such a hybrid system and synthesize it with fault-tolerant protocols by combining bosonic quantum error correction (QEC) and advanced quantum control techniques. We introduce essential building blocks of error-corrected gadgets by leveraging ancilla-assisted bosonic operations using a generalized variant of path-independent quantum control. Using these building blocks, we construct a universal set of error-corrected gadgets that tolerate a single-photon loss and an arbitrary ancilla fault for four-legged cat qubits. Notably, our construction requires only dispersive coupling between bosonic modes and ancillae, as well as beam-splitter coupling between bosonic modes, both of which have been experimentally demonstrated with strong strengths and high accuracy. Moreover, each error-corrected bosonic qubit is comprised of only a single bosonic mode and a three-level ancilla, featuring the hardware efficiency of bosonic QEC in the full fault-tolerant setting. We numerically demonstrate the feasibility of our schemes using current experimental parameters in the circuit-QED platform. Finally, we present a hardware-efficient architecture for fault-tolerant quantum computing by concatenating the four-legged cat qubits with an outer qubit code utilizing only beam-splitter couplings. Our estimates suggest that the overall noise threshold can be reached using existing hardware. These developed fault-tolerant schemes extend beyond their applicability to four-legged cat qubits and can be adapted for other rotation-symmetrical codes, offering a promising avenue toward scalable and robust quantum computation with bosonic qubits. Published by the American Physical Society 2024
Physics↗
Absence of magnetic order in epitaxial RuO 2 revealed by x-ray linear dichroism
Recently the topic of altermagnetism has attracted tremendous attention, and RuO 2 has been demonstrated to be one of the most promising altermagnetic candidates. However, disputes still remain on the existence of magnetic order in RuO 2 . Here in this work, we employ x-ray linear dichroism (XLD), a widely utilized technique for characterizing antiferromagnets, in conjunction with photoemission electron microscopy and multiple scattering calculation to provide clear evidence of the absence of magnetic order in epitaxial RuO 2 films. The observed XLD signal is nearly invariant with temperature and independent of cooling-field direction, in stark contrast to the substantial magnetic-order-related XLD signal predicted by multiple scattering calculation. This finding strongly suggests a nonmagnetic origin for RuO 2 . Furthermore, we observed significantly distinct XLD signals at the Ru M 3 and O K edges in RuO 2 films grown on TiO 2 substrate with different surface orientations, which can be attributed to the low-symmetry crystal field. In conclusion, these results unequivocally demonstrate the absence of magnetic order in RuO 2 and establish XLD measurement as a robust technique for probing the low-symmetry magnetic materials.
Wang, Siyu [University of Science and Technology o↗
Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit
Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.
Yin, Junqi [ORNL] (ORCID:0000000338435520)↗
Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility
This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.
deep reinforcement learning↗
MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training
Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.
Bae, Jonghyun↗
Analytic Neural Network Gaussian Process Enabled Chance-Constrained Voltage Regulation for Active Distribution Systems with PVs, Batteries and EVs
This paper proposes an analytic neural network Gaussian process (NNGP)-based chance-constrained real-time voltage regulation method for active distribution systems with photovoltaics (PVs), batteries, and electric vehicles (EVs). NNGP can utilize historical measurement data to achieve real-time probabilistic node voltage estimation through Bayesian inference. Then, NNGP is fully analytically embedded into the optimal power flow model to perform voltage regulation and adapt to various topological changes. The uncertainties of voltage estimations are easily considered via the chance constraint, and it has been shown that the adoption of this chance constraint can significantly improve the reliability of voltage regulation under various scenarios. The comparison results with other methods, carried out on a real 759-node distribution system located in western Colorado, U.S., show that the proposed method can achieve accurate voltage estimation across different topologies and reliably perform voltage regulation considering PVs, batteries, and EVs.
active distribution systems↗
Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs
As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Smaller feature size impairs densification in digital light processing 3D printed ceramic samples
In this study, we investigate how geometric miniaturization affects the sintering densification behavior of alumina cylinders with various wall thicknesses (down to ∼500 µm in green body) fabricated via digital light processing (DLP). As the wall thickness decreased, the density of the sintered samples consistently declined. Microstructural characterization revealed the presence of a crack-rich surface skin that is ∼500 µm thick. As the wall thickness decreases, the skin's volume fraction rises, and its poor local densification increasingly dominates the part average, leading to an overall decrease in the density of the sintered samples. For the thinnest wall (500 µm) cylinder, macroscopic warping occurred during sintering. We suggest that warping further disrupts particle packing and induces through-thickness nonuniformity, further impairing densification in addition to the skin-fraction effect. These results highlight how geometric miniaturization may adversely influence the sintering behavior and final density of DLP-printed ceramics through distinct densification-limiting mechanisms.
Materials science↗