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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine Learning an Ab-Initio Based Bond-Order Potential for Bismuthene

Bismuthene is a heavy 2D material whose strong spin–orbit coupling and recently observed single-element ferroelectricity have intensified interest in its structural, vibrational, and transport properties. Accurate modeling of these behaviors requires a short-range interatomic potential that can reproduce the underlying bonding physics at a fraction of the computational cost of first-principles methods. However, such a potential is currently unavailable. Here, in this work, we construct a Tersoff bond-order potential for β-bismuthene using a reinforcement-learning framework that integrates a continuous Monte Carlo Tree Search with a simplex-based local optimizer. The optimized parameter sets reproduce first-principles lattice constants, cohesive energy, the equation of state, elastic constants, and phonon dispersion. We validate the models by performing thermal-conductivity calculations and uniaxial fracture simulations our findings confirm the reliability of the resulting models across multiple thermomechanical regimes. Comparison of the three best solutions reveals how differences in pairwise interactions, angular terms, and bond-order behavior govern phonon features and mechanical responses. We demonstrate an interpretable and computationally efficient potential for bismuthene and demonstrate a general reinforcement-learning strategy for developing bond-order models in emerging 2D materials.

deformation↗
DOE OSTI2024

Search for dark QCD with emerging jets in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

A search for “emerging jets” produced in proton-proton collisions at a center-of-mass energy of 13 TeV is performed using data collected by the CMS experiment corresponding to an integrated luminosity of 138 fb$^{−1}$. This search examines a hypothetical dark quantum chromodynamics (QCD) sector that couples to the standard model (SM) through a scalar mediator. The scalar mediator decays into an SM quark and a dark sector quark. As the dark sector quark showers and hadronizes, it produces long-lived dark mesons that subsequently decay into SM particles, resulting in a jet, known as an emerging jet, with multiple displaced vertices. This search looks for pair production of the scalar mediator at the LHC, which yields events with two SM jets and two emerging jets at leading order. The results are interpreted using two dark sector models with different flavor structures, and exclude mediator masses up to 1950 (1950) GeV for an unflavored (flavor-aligned) dark QCD model. The unflavored results surpass a previous search for emerging jets by setting the most stringent mediator mass exclusion limits to date, while the flavor-aligned results provide the first direct mediator mass exclusion limits to date.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Discovering Classical Spin Liquids by Topological Search of High Symmetry Nets

Spin liquids are a paradigmatic example of a nontrivial state of matter. The search for new spin liquids is a key interdisciplinary challenge. Geometrical frustration-where the geometry of the net that the spins occupy precludes the generation of a simple ordered state-is a particularly fruitful way to generate these intrinsically disordered states. Prior focus has been on a handful of high symmetry nets. There are, however, many three-dimensional nets, each of which has the potential to form unique states. In this paper, we investigate the high symmetry nets-those which are both vertex- and edge-transitive-for the simplest possible interaction sets: nearest-neighbor couplings of antiferromagnetic Heisenberg and Ising spins. While the well-known crs (pyrochlore) net is the only nearest-neighbor Heisenberg antiferromagnet which does not order, we identify two new frustrated nets (lcx and thp) possessing finite temperature Heisenberg spin-liquid states with strongly suppressed magnetic ordering and noncollinear ground states. With Ising spins, we identify three new classical spin liquids that do not order down to T/J = 0.01. We highlight materials that contain these high symmetry nets, and which could, if substituted with appropriate magnetic ions, potentially host these unusual states. Our systematic survey will guide searches for novel magnetic phases.

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

Time-delayed gamma-ray signatures of heavy axions from core-collapse supernovae

Heavy axions that couple to both quantum electrodynamics and quantum chromodynamics with masses on the order of MeV–GeV and high-scale decay constants in excess of ∼ 10 8 GeV may arise generically in, e.g., axiverse constructions. In this work we provide the most sensitive search to date for the existence of such heavy axions using Fermi-LAT data toward four recent supernovae (SN): Cassiopea A, SN1987A, SN2023ixf, and SN2024ggi. We account for heavy axion production in the proto-neutron-star cores through nuclear and electromagnetic processes and then the subsequent decay of the axions into photons. While previous works have searched for gamma rays from SN1987A using the Solar Maximum Mission that observed SN1987A during the SN itself, we show that using Fermi Large Area Telescope data provides an approximately 5 orders of magnitude improvement in flux sensitivity for axions with lifetimes larger than around 10 yrs. We find no evidence for heavy axions and exclude large regions of previously unexplored parameter space.

Axions↗
DOE OSTI2026

Hidden-sectors search and probe of discrete symmetries at the REDTOP experiment

The $η$ and $η^{\prime}$ mesons are nearly unique in the particle universe since they are nearly Goldstone bosons, and their decay dynamics are strongly constrained. While earlier experiments collected samples of order $\sim 10^{9}η$, the proposed REDTOP (Rare Eta Decays To Observe Physics Beyond the Standard Model) facility targets $\mathcal{O}(10^{14})η$ and $\mathcal{O}(10^{12})η^\prime$, enabling broad searches for physics beyond the Standard Model. In this work, we present studies evaluating REDTOP sensitivity to processes that couple the Standard Model to New Physics through four portals: the Vector (dark photon), the Scalar (Higgs-mixing), the Axion-like, and the Heavy Lepton. In parallel, the proposed statistics allow precise tests of $CP$ and $T$ invariance and lepton universality and improve determinations of the $η/η'$ transition form factors, which are crucial inputs to the hadronic light-by-light contribution to the muon anomalous magnetic moment $(g-2)_μ$.

Gatto, C. [INFN, Naples; Northern Illinois U.]↗
DOE OSTI2026

Exploring AI/ML-based Real-time Anomaly Detection in DUNE for Supernova Burst Neutrinos

The Deep Underground Neutrino Experiment (DUNE) is currently under construction with far detectors consisting of 4 liquid argon time projection chamber (LArTPC) modules at SURF (South Dakota Underground Research Facility) and a near detector complex with neutrino beam production at Fermilab to unambiguously determine neutrino mass ordering, to discover and precisely measure Charge-Parity (CP) violation phase in leptonic sector, to search for Beyond Stand Model (BSM) physics, and to study solar and supernova burst neutrinos. Anomalies in this project are classified in three categories: new physics signals, supernova burst neutrinos, and detector malfunction. We report here on promising early studies toward an Artificial Intelligence/Machine Learning-based real-time anomaly detection system, using a prototype autoencoder model currently under development. Additionally, the current status of an improved model and its performance will be presented. The model will be evaluated not only for its sensitivity to supernova neutrinos, but also to BSM physics signals and detector malfunctions. We will also consider how such a real-time algorithm might be used in DUNE.

de Jonge, Anselm [Kirchhoff Inst. Phys.] (ORCID:00↗
DOE OSTI2024

Simulating the Phonon Collection Efficiency in KIPMDs

Kinetic inductance phonon-mediated (KIPM) detectors use microwave kinetic inductance detectors (MKIDs) to read out phonon signals in the substrate. They are a promising class of detectors to be used in light dark matter (DM) searches due to their potential eV-scale sensitivity and native frequency-domain multiplexability. In order to improve upon the design of these detectors, simulations are needed to understand the effects of detector design on measurable physical parameters including the phonon collection efficiency, $\eta_{ph}$, which is defined as the ratio of phonon energy detected by the sensitive target element to the incident energy deposited in the substrate. This work simulates the phonon collection efficiency for a KIPMD currently operated at the Northwestern EXperimental Underground Site (NEXUS).

Dang, Stella Q.↗
DOE OSTI2024

Simulating the Phonon Collection Efficiency in Kinetic Inductance Phonon-Mediated Detectors

Kinetic inductance phonon-mediated (KIPM) detectors use microwave kinetic inductance detectors (MKIDs) to read out phonon signals in the substrate. They are a promising class of detectors to be used in light dark matter (DM) searches due to their potential eV-scale sensitivity and native frequency-domain multiplexability. In order to improve upon the design of these detectors, simulations are needed to understand the effects of detector design on measurable physical parameters including the phonon collection efficiency, $\eta_{ph}$, which is defined as the ratio of phonon energy detected by the sensitive target element to the incident energy deposited in the substrate. This work simulates the phonon collection efficiency for two existing detector architectures to study phonon absorption at substrate-sensor interfaces and phonon loss to non-sensitive detector elements.

Dang, Stella Q.↗
DOE OSTI2025

Search for an Anomalous Excess of Single Photons in the MicroBooNE Neutrino Experiment

Neutrinos are some of the most elusive particles in the standard model, being incredibly common throughout the universe, but interacting with detectors incredibly rarely. Certain properties of neutrinos remain difficult to measure, including their masses, their CP violation properties, and whether or not they are their own antiparticles. Additionally, there have been several anomalous results in neutrino experiments which remain unexplained. MicroBooNE was built in order to study these anomalous results using a more capable detector technology, the Liquid Argon Time Projection Chamber. Specifically, MicroBooNE is able to search for an anomalous excess of low energy electromagnetic showers, which was previously observed by the MiniBooNE experiment. In particular, MicroBooNE is able to study whether the excess could consist of electron showers or photon showers. In this thesis, I describe a search for this anomalous excess by targeting neutral current Delta radiative decays, the largest expected source of single photons in MicroBooNE. We observe data consistent with our nominal expectation, but cannot rule out all potential sources of additional single photon events, particularly those with no visible proton activity. There remains significant potential to probe this channel in even more detail using MicroBooNE and other experiments in the near future.Hagaman, Lee

Hagaman, Lee [Chicago U.]↗
DOE OSTI2026

Hierarchical Reinforcement Learning of a Short-Range Bond-Order Potential for Silica: Analytic Embedding of Coordination with Classical Efficiency

Reinforcement learning (RL) has recently emerged as a data-efficient strategy to parametrize short-range interatomic potentials. Building on our past RL optimization of pairwise silica models, we extend the framework to a bond-order (Tersoff-type) potential that provides an analytic embedding of local coordination through a three-body term. A hierarchical RL workflow combining continuous-action Monte Carlo Tree Search and property-based rewards efficiently explores the 26-dimensional parameter space, sequentially optimizing lattice parameters, densities, angles, and cohesive energies of 21 silica polymorphs. The resulting models, Q-Tersoff and ML-Tersoff, reproduce the energetic ordering of low-energy phases and capture the angular correlations and amorphous structure factors of silica with improved fidelity over pairwise force fields, while remaining orders of magnitude faster than high-dimensional machine-learned potentials. Both models underperform for elastic constants and high-energy frameworks, delineating the limits of the current analytic form. The approach establishes a general and interpretable route to angle-aware, short-range potentials that bridge physics-based and machine-learned descriptions of silicate materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Flaring Stars in a Non-targeted mm-wave Survey with SPT-3G

We present a flare star catalog from four years of non-targeted millimeter-wave survey data from the South Pole Telescope (SPT). The data were taken with the SPT-3G camera and cover a 1500-square-degree region of the sky from $20^{h}40^{m}0^{s}$ to $3^{h}20^{m}0^{s}$ in right ascension and $-42^{\circ}$ to $-70^{\circ}$ in declination. This region was observed on a nearly daily cadence from 2019-2022 and chosen to avoid the plane of the galaxy. A short-duration transient search of this survey yields 111 flaring events from 66 stars, increasing the number of both flaring events and detected flare stars by an order of magnitude from the previous SPT-3G data release. We provide cross-matching to Gaia DR3, as well as matches to X-ray point sources found in the second ROSAT all-sky survey. We have detected flaring stars across the main sequence, from early-type A stars to M dwarfs, as well as a large population of evolved stars. These stars are mostly nearby, spanning 10 to 1000 parsecs in distance. Most of the flare spectral indices are constant or gently rising as a function of frequency at 95/150/220 GHz. The timescale of these events can range from minutes to hours, and the peak $\nu L_{\nu}$ luminosities range from $10^{27}$ to $10^{31}$ erg s$^{-1}$ in the SPT-3G frequency bands.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning

Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order–disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.

Chemistry↗
DOE OSTI2024

Search for Heavy Neutral Leptons at the MINER$\nu$A detector

Heavy Neutral Leptons (HNL) are particles hypothesised to provide a mass generation mechanism for the active (observed) neutrino species, which are known to have nonzero mass from the definitive observation of neutrino oscillations. HNL are eigenstates of mass of the order $\mathcal{O}(0.1 − 1\,\,\mathrm{GeV}/c^{2})$, which mix into the active flavour eigenstates through the extended leptonic mixing matrix. Apart from neutrino mass, they could provide a natural dark matter candidate and a mechanism for matter-antimatter asymmetry in the early Universe, giving rise to today’s matter-dominated cosmos. Searches for HNL typically attempt to either confirm the existence of HNL through an excess in data that is most compatible with an HNL hypothesis, or by setting limits on the HNL parameter space $\left(M_{N4}, \left|U_{\alpha 4}\right|^{2}\right)$ in the case of statistically insignificant excess. Such searches are intensifying as part of a global research programme at both colliders and accelerator / atmospheric neutrino experiments. One such setting is the MINER$\nu$A experiment, located in the NuMI beamline at Fermilab. MINER$\nu$A has collected a large amount of data over seven years of operation to measure the cross-sections of neutrino-nucleus interactions necessary to drive systematic uncertainties down, in order for neutrino oscillation experiments to achieve sensitivity to CP violation in the neutrino sector. With the high-energy, high-intensity NuMI beam, and with good timing, position, and angular resolution leading to sensitivity to HNL decays, MINER$\nu$A is fertile ground for an HNL search, which is performed in this thesis. Specifically, a novel, experiment-agnostic and general simulation of HNL production and decay is presented and deployed; this simulation has been incorporated in the ubiquitous GENIE neutrino event generator for use with neutrino experiments in the future. The event selection and background characterisation is discussed in detail, including the primary background coming from charged-current coherent and diffractive pion production from neutrino-nucleus interactions, and constraints on the background using control regions from MINER$\nu$A data are derived. Finally, by means of a fake-data study given a background of $\mathcal{O}(500)$ events, the discovery potential and limit-setting capacity of MINER$\nu$A is demonstrated, and ways to improve this capacity are expounded upon.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Multiscale astrobiology with the Vera C. Rubin Observatory Legacy Survey of Space and Time

The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) presents an unprecedented opportunity to advance the search for astrobiologically relevant data across diverse astronomical environments. Through its extensive imaging capabilities, LSST will enhance our inventory of Solar System objects and assess their potential astrobiological conditions. Beyond the Solar System, LSST will survey billions of stars, contributing to the detection of exoplanets, characterizing planetary atmospheres at a zeroth-order level, identifying transient phenomena, and exploring conditions within habitable zones. In this paper, we highlight LSST’s unique contribution to astrobiology, complementing other missions and expanding the search for life beyond Earth in the coming decades.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The Mu2e straw tracker detector status and prototype results

The Mu2e experiment will search for charge-lepton flavor violating (CLFV) muon to electron conversion. It aims to achieve a four-orders of magnitude improvement in sensitivity over previous experiments, allowing it to probe new physics at mass scales up to 10 4 TeV . A precision momentum measurement is needed to resolve the monoenergetic electron that is the signal of CLFV conversion from muon decay-in-orbit backgrounds. Here, this is achieved in Mu2e using a low-mass cylindrical straw tracker operated in vacuum, consisting of 21,000 thin-wall mylar straws held at tension. The Mu2e tracker is now in production and will be completed during 2025. We will discuss the design and status of the experiment and the tracker detector, and show results from data taken with the first tracker module.

Gaseous detector↗
DOE OSTI2026

Impact of new physics on the JUNO-long-baseline synergy in the neutrino mass ordering determination

The determination of the neutrino mass ordering is one of the flagship goals in particle physics. A well-known and powerful synergy emerges when combining high-precision measurements of the effective atmospheric mass-squared splitting from electron antineutrino disappearance in reactor experiments with that from muon (anti)neutrino disappearance in accelerator-based long-baseline experiments. To fully exploit this synergy, percent-level precision in the atmospheric mass splitting is required—a target that JUNO is expected to achieve within a few months of data taking. This motivated the formulation of a mass ordering sum rule for neutrino disappearance channels, which shows that by combining data from T2K and NOvA with JUNO after one year of operation, the neutrino mass ordering can be determined at the 3⁢𝜎 confidence level. Since JUNO has recently started taking data, it is timely to ask whether this sum rule remains robust in the presence of new physics. We identify the necessary conditions for new physics to affect the sum rule and demonstrate that, in some cases, such effects could lead to an incorrect inference of the mass ordering. As concrete examples, we consider scalar nonstandard interactions (SNSI) and neutrinos coupled to an ultralight scalar field. We find that, for SNSI, current constraints render any modification of the sum rule negligible, whereas in the latter case, the inference of the ordering requires caution. Nevertheless, these effects can be disentangled, illustrating how the sum rule can also be used to search for new physics.

Alves, Gustavo F. S. [Fermi National Accelerator L↗
DOE OSTI2025

Quantum stabilization of unexpected ordered phases on the honeycomb lattice

In this work, I discuss and showcase the utility of the method called minimally-augmented spin-wave theory (MAGSWT) as a relatively simple semi-analytical approach to the phase diagrams of quantum magnets. It complements numerical methods by providing physical insight into which states are competitive and by yielding approximate phase boundaries that agree well with much more numerically intensive calculations. This approach enabled me to construct the faithful phase diagrams of two paradigmatic honeycomb-lattice models in the quantum S=1/2 limit, the J 1 –J 3 FM-AF and J 1 –J 2 AF models, for the collinear quantum phases that replace or extend the classical ones. The results are in good qualitative and semi-quantitative agreement with state-of-the-art numerical studies for these models, correctly capturing the emergence of unexpected quantum phases and the suppression of classically favored spiral orders by quantum fluctuations. This study provides a much-needed important guidance to the ongoing theoretical and experimental searches of the unconventional quantum states. This work will be of significant and timely interest to both theorists and experimentalists in the field of quantum magnetism, broadly defined.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗