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Centrosymmetric compounds which host three-dimensional topological spin structures comprise a distinct subclass of materials in which multiple-$\mathbf{q}$ magnetic order is stabilized by anisotropy and bond frustration in contrast to the more common path of antisymmetric exchange interactions. Here we investigate static and dynamic magnetic properties of the cubic perovskite SrFeO$_3$ $\unicode{x2013}$ a rare example of a centrosymmetric material hosting two types of topological spin textures: skyrmion- and hedgehog-lattice phases. Our detailed magnetization and dilatometry measurements describe the domain selection processes and phase transitions in SrFeO$_3$. Spin excitations are investigated using inelastic neutron scattering for all three zero-field phases. In the higher-temperature ordered phases, high-energy magnons increasingly lose coherence, so that spin fluctuations are dominated by a distinct quasielastic component at low energies. We anticipate that this could be generic to symmetric helimagnets in which the chiral symmetry is spontaneously broken by the magnetic order.

We have investigated the magnetic diffuse scattering of isostructural $S$ = 5/2 fcc antiferromagnets Ba$_2$MnTeO$_6$ and Ba$_2$MnWO$_6$ using polarized neutrons. Both materials display short-range correlated magnetism above their respective magnetic ordering temperatures of 20 K and 8 K. The spin correlations were analysed using a Reverse Monte Carlo approach. For Ba$_2$MnTeO$_6$, we find antiferromagnetic nearest-neighbor correlations along with ferromagnetic next-nearest neighbor correlations directly linked to the Type I order below $T_N$. For Ba$_2$MnWO$_6$, both the nearest-neighbor and next-nearest-neighbor spin correlations are antiferromagnetic in the paramagnetic state. The short-range spin correlations persist up to $T$ = 7$T_N$. The magnetic diffuse scattering was also fitted using Onsager reaction-field theory allowing us to evaluate the magnetic interactions in these materials. We obtained $J_1$ = -3.25(3) K and $J_2$ = 0.41(2) K for Ba$_2$MnTeO$_6$ and $J_1$ = -1.08(1) K and $J_2$ = -0.88(1) K for Ba$_2$MnWO$_6$. These interactions are comparable to previous results from inelastic neutron scattering experiments below $T_N$, which highlights the potential of the Onsager approach for the analysis of magnetic interactions., Comment: 21+4 pages, 5+3 figures

After several decades of studies of high-temperature superconductivity, there is no compelling theory for the mechanism yet; however, the spin fluctuations have been widely believed to play a crucial role in forming the superconducting Cooper pairs. The recent discovery of high-temperature superconductivity near 80 K in the bilayer nickelate La$_3$Ni$_2$O$_7$ under pressure provides a new platform to elucidate the origins of high-temperature superconductivity. We perform elastic and inelastic neutron scattering studies on a polycrystalline sample of La$_3$Ni$_2$O$_{7-\delta}$ at ambient pressure. No magnetic order can be identified down to 10 K. The absence of long-range magnetic order in neutron diffraction measurements may be ascribed to the smallness of the magnetic moment. However, we observe a weak flat spin-fluctuation signal at $\sim$ 45 meV in the inelastic scattering spectra. The observed spin excitations could be interpreted as a result of strong interlayer and weak intralayer magnetic couplings for stripe-type antiferromagnetic orders. Our results provide crucial information on the spin dynamics and are thus important for understanding the superconductivity in La$_3$Ni$_2$O$_7$., Comment: 10 pages, 9 figures with supplementary information

The unusual quantum-disordered magnetic ground state intertwined with superconductivity and electronic nematicity in FeSe has been a research focus in iron-based superconductors. However, the intrinsic spin excitations across the entire Brillouin zone in detwinned FeSe, which forms the basis for a microscopic understanding of the magnetic state and superconductivity, remain to be determined. Here, we use inelastic neutron scattering to map out the spin excitations of FeSe dewtinned with a uniaxial-strain device. We find that the stripe spin excitations (Q=(1, 0)/(0, 1)) exhibit the $C_2$ symmetry up to $E\approx120$ meV, while the N{\'e}el spin excitations (Q=(1, 1)) retain their $C_4$ symmetry in the nematic state. The temperature dependence of the difference in the spin excitations at Q=(1, 0) and (0, 1) for temperatures above the structural phase transition unambiguously shows the establishment of the nematic quantum disordered state. The similarity of the N\'eel excitations in FeSe and NaFeAs suggests that the N\'eel excitations are driven by the enhanced electron correlations in the $3d_{xy}$ orbital. By determining the key features of the stripe excitations and fitting their dispersions using a Heisenberg Hamiltonian with biquadratic interaction ($J_1$-$K$-$J_2$), we establish a spin-interaction phase diagram and conclude that FeSe is close to a crossover region between the antiferroquadrupolar, N\'eel, and stripe ordering regimes. The results provide an experimental basis for establishing a microscopic theoretical model to describe the origin and intertwining of the emergent orders in iron-based superconductors., Comment: 9 pages, 5 figures

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions.

Quantum spin liquids are highly entangled magnetic states with exotic properties. The $S = 1/2$ square-lattice Heisenberg model is one of the foundational models in frustrated magnetism with a predicted, but never observed, quantum spin liquid state. Isostructural double perovskites Sr$_2$CuTeO$_6$ and Sr$_2$CuWO$_6$ are physical realizations of this model, but have distinctly different types magnetic order and interactions due to a $d^{10}/d^0$ effect. Long-range magnetic order is suppressed in the solid solution Sr$_2$CuTe$_{1-x}$W$_x$O$_6$ in a wide region of $x = 0.05-0.6$, where the ground state has been proposed to be a disorder-induced spin liquid. Here we show that the spin-liquid-like $x = 0.2$ and $x = 0.5$ samples have distinctly different local spin correlations, which suggests they have different ground states. Furthermore, the previously ignored interlayer coupling between the square-planes is likely to play a role in the suppression of magnetic order on the W-rich side at $x \approx 0.6$. These results highlight the complex magnetism of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$ and hint at a new quantum critical point at $x \approx 0.3$., Comment: 19+8 pages, 6+8 figures

The response of metals and their microstructures under extreme dynamic conditions can be markedly different from that under quasistatic conditions. Traditionally, high strain rates and shock stresses are measured using cumbersome and expensive methods such as the Kolsky bar or large spall experiments. These methods are low throughput and do not facilitate high-fidelity microstructure-property linkages. In this work, we combine two powerful small-scale testing methods, custom nanoindentation, and laser-driven micro-flyer shock, to measure the dynamic and spall strength of metals. The nanoindentation system is configured to test samples from quasistatic to dynamic strain rate regimes (10$^{-3}$ s$^{-1}$ to 10$^{+4}$ s$^{-1}$). The laser-driven micro-flyer shock system can test samples through impact loading between 10$^{+5}$ s$^{-1}$ to 10$^{+7}$ s$^{-1}$ strain rates, triggering spall failure. The model material used for testing is Magnesium alloys, which are lightweight, possess high-specific strengths and have historically been challenging to design and strengthen due to their mechanical anisotropy. Here, we modulate their microstructure by adding or removing precipitates to demonstrate interesting upticks in strain rate sensitivity and evolution of dynamic strength. At high shock loading rates, we unravel an interesting paradigm where the spall strength of these materials converges, but the failure mechanisms are markedly different. Peak aging, considered to be a standard method to strengthen metallic alloys, causes catastrophic failure, faring much worse than solutionized alloys. Our high throughput testing framework not only quantifies strength but also teases out unexplored failure mechanisms at extreme strain rates, providing valuable insights for the rapid design and improvement of metals for extreme environments.

The Global mm-VLBI Array (GMVA) has demonstrated the ability to resolved what may be the general relativistic shadow of the supermassive black hole in M87 at 86 GHz, as well as delineate the inner jet to $\sim 1$~mas distance. We investigate the ability of the planned ngEHT, and the ngEHT + ngVLA at 85 GHz, to image such a nuclear 'ring', and the associated jet, using a constructed model based on the current estimate of the ring size, and a scaled version of the best VLBA image of the M87 jet at 43 GHz. While the resolution does not improve due to the limit set by the diameter of the Earth, the ngEHT alone should provide both a higher fidelity image of the ring on scales $\le 0.1$~mas, and a good image of a more extended jet to $\sim 1$~mas. Adding the ngVLA improves substantially the dynamic range (factor 3.5), as well as adds the ability to image structures on larger scales, in this case out to at least 5~mas, and potentially to much larger scales given the $\sim 10^5$ range in spatial scales covered by the ngVLA itself. Both arrays provide good image fidelity ($\le 0.1$), in the inner $\sim 1$~mas, but the ngEHT-only image does not reproduce the outer jet well, or at all, with fidelity values greater than unity. The combined array reproduces much of the outer jet with good fidelity ($\le 0.3$). Adding the ngVLA also decreases the susceptibility to antenna-based phase errors by a similar factor, and improves the ability for fringe fitting and subsequent phase and amplitude self-calibration. As for scales $< 100~\mu$as, ie. the ring itself, adding the ngVLA makes little change for very bright sources, where uniform weighting can be employed. But for faint sources, adding the ngVLA adds potentially an order-of magnitude sensitivity improvement (Issaoun et al. 2023)., Comment: 14 pages, 5 figures, ngVLA memo 118

We review well-known signatures of disorder in crystallographic and inelastic neutron scattering data. We show that these can arise from different types of disorder, corresponding to different values of the system entropy. Correlating the entropy of a material with its atomistic structure and dynamics is in general a difficult problem that requires correlating information between multiple experimental techniques including crystallography, spectroscopy, and calorimetry. These comments are illustrated with particular reference to barocalorics, but are relevant to a broad range of calorics and other disordered crystalline materials., Comment: 11 pages, 3 figures

The need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification. Class Activation Maps (CAMs) are an increasingly popular category of visual explanation methods for Convolutional Neural Networks (CNNs). However, the performance of individual CAMs depends largely on experimental parameters such as the selected image, target class, and model. Here, we propose MetaCAM, an ensemble-based method for combining multiple existing CAM methods based on the consensus of the top-k% most highly activated pixels across component CAMs. We perform experiments to quantifiably determine the optimal combination of 11 CAMs for a given MetaCAM experiment. A new method denoted Cumulative Residual Effect (CRE) is proposed to summarize large-scale ensemble-based experiments. We also present adaptive thresholding and demonstrate how it can be applied to individual CAMs to improve their performance, measured using pixel perturbation method Remove and Debias (ROAD). Lastly, we show that MetaCAM outperforms existing CAMs and refines the most salient regions of images used for model predictions. In a specific example, MetaCAM improved ROAD performance to 0.393 compared to 11 individual CAMs with ranges from -0.101-0.172, demonstrating the importance of combining CAMs through an ensembling method and adaptive thresholding., Comment: 9 pages

Compartmental models are valuable tools for investigating infectious diseases. Researchers building such models typically begin with a simple structure where compartments correspond to individuals with different epidemiological statuses, e.g., the classic SIR model which splits the population into susceptible, infected, and recovered compartments. However, as more information about a specific pathogen is discovered, or as a means to investigate the effects of heterogeneities, it becomes useful to stratify models further -- for example by age, geographic location, or pathogen strain. The operation of constructing stratified compartmental models from a pair of simpler models resembles the Cartesian product used in graph theory, but several key differences complicate matters. In this article we give explicit mathematical definitions for several so-called ``model products'' and provide examples where each is suitable. We also provide examples of model stratification where no existing model product will generate the desired result.

If the construction of the ngVLA begins in 2026, its sensitivity is expected to match that of the VLA by late 2029. At that juncture it is anticipated that open-skies observing will cease on the VLA and commence on the ngVLA. We suggest that during 2026-2029 the VLA be held in a customized final configuration encompassing portions of its standard A, B, C and D configurations. Such a final VLA configuration would (1) help minimize the cost of VLA operations and maximize the pace of ngVLA construction and commissioning; (2) help VLA users pivot to the high-resolution, high-frequency research topics that are projected to headline the ngVLA science program; and (3) help mitigate the effects of source confusion during responses to transients in the era of the Rubin Observatory and LIGO A+., Comment: 7 pages, 9 figures; received 2022 May 13 as ngVLA Memo # 97 for consideration by the VLA/VLBA to ngVLA Transition Advisory Group

In the rapidly expanding field of topological materials there is growing interest in systems whose topological electronic band features can be induced or controlled by magnetism. Magnetic Weyl semimetals, which contain linear band crossings near the Fermi level, are of particular interest owing to their exotic charge and spin transport properties. Up to now, the majority of magnetic Weyl semimetals have been realized in ferro- or ferrimagnetically ordered compounds, but a disadvantage of these materials for practical use is their stray magnetic field which limits the minimum size of devices. Here we show that Weyl nodes can be induced by a helical spin configuration, in which the magnetization is fully compensated. Using a combination of neutron diffraction and resonant elastic x-ray scattering, we find that EuCuAs develops a planar helical structure below $T_\textrm{N}$ = 14.5 K which induces Weyl nodes along the $\Gamma$--A high symmetry line in the Brillouin zone.

We use inelastic neutron scattering to study spin fluctuations in Sr$_{1.8}$La$_{0.2}$RuO$_4$, where Lanthanum doping triggers a Lifshitz transition by pushing the van Hove singularity in the $\gamma$ band to the Fermi energy. Strong spin fluctuations emerge at an incommensurate wave vector $\mathbf{Q}_{ic} = (0.3,0.3)$, corresponding to the nesting vector between $\alpha$ and $\beta$ Fermi sheets. The incommensurate antiferromagnetic fluctuations shift toward $(0.25,0.25)$ with increasing energy up to ${\sim}110$ meV. By contrast, scatterings near the ferromagnetic wave vectors $\mathbf{Q} = (1,0)$ and $(1,1)$ remain featureless at all energies. This contradicts the weak-coupling perspective that suggests a sharp enhancement of ferromagnetic susceptibility due to the divergence of density of states in the associated $\gamma$ band. Our findings imply that ferromagnetic fluctuations in Sr$_2$RuO$_4$ and related materials do not fit into the weak-coupling paradigm, but instead are quasi-local fluctuations induced by Hund's coupling. This imposes significant constraints for the pairing mechanism involving spin fluctuations., Comment: 7 pages, 4 figures

When investigating suitability for surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation and resection of the epileptogenic zone (EZ), and improve surgical outcomes in epilepsy. We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. For all patients, T1-weighted and diffusion-weighted MRIs were acquired prior to iEEG implantation. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls respectively. First, we explored whether the resection of maximal (dMRI and iEEG) abnormalities related to improved surgical outcomes. Second, we investigated whether the modalities provided complementary information for improved prediction of surgical outcome. Third, we suggest how dMRI abnormalities may be useful to inform the placement of iEEG electrodes as part of the pre-surgical evaluation using a patient case study. Seizure freedom was 15 times more likely in those patients with resection of maximal dMRI and iEEG abnormalities (p=0.008). Both modalities were separately able to distinguish patient outcome groups and when combined, a decision tree correctly separated 36 out of 43 (84%) patients based on surgical outcome. Structural dMRI could be used in pre-surgical evaluations, particularly when localisation of the EZ is uncertain, to inform personalised iEEG implantation and resection., Comment: 5 figures

We use a combination of variable-temperature / variable-pressure neutron powder diffraction, variable-pressure inelastic neutron scattering, and quantum chemical calculations to interrogate the behaviour of the negative thermal expansion (NTE) material $^{114}$Cd(CN)$_2$ under hydrostatic pressure. We determine the equation of state of the ambient-pressure phase, and discover the so-called `warm hardening' effect whereby the material becomes elastically stiffer as it is heated. We also identify a number of high-pressure phases, and map out the phase behaviour of Cd(CN)$_2$ over the range $0\leq p\leq0.5$\,GPa, $100\leq T\leq300$\,K. As expected for an NTE material, the low-energy phonon frequencies are found to soften under pressure, and we determine an effective Gr{\"u}neisen parameter for these modes. Finally, we show that the elastic behaviour of Cd(CN)$_2$ is sensitive to the local Cd coordination environment, which suggests an interplay between short- (phononic) and long-timescale (cyanide flips) fluctuations in Cd(CN)$_2$.

Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method, Semantic Positives via Pseudo-Labels (SemPPL), that combines labelled and unlabelled data to learn informative representations. Our method extends self-supervised contrastive learning -- where representations are shaped by distinguishing whether two samples represent the same underlying datum (positives) or not (negatives) -- with a novel approach to selecting positives. To enrich the set of positives, we leverage the few existing ground-truth labels to predict the missing ones through a $k$-nearest neighbours classifier by using the learned embeddings of the labelled data. We thus extend the set of positives with datapoints having the same pseudo-label and call these semantic positives. We jointly learn the representation and predict bootstrapped pseudo-labels. This creates a reinforcing cycle. Strong initial representations enable better pseudo-label predictions which then improve the selection of semantic positives and lead to even better representations. SemPPL outperforms competing semi-supervised methods setting new state-of-the-art performance of $68.5\%$ and $76\%$ top-$1$ accuracy when using a ResNet-$50$ and training on $1\%$ and $10\%$ of labels on ImageNet, respectively. Furthermore, when using selective kernels, SemPPL significantly outperforms previous state-of-the-art achieving $72.3\%$ and $78.3\%$ top-$1$ accuracy on ImageNet with $1\%$ and $10\%$ labels, respectively, which improves absolute $+7.8\%$ and $+6.2\%$ over previous work. SemPPL also exhibits state-of-the-art performance over larger ResNet models as well as strong robustness, out-of-distribution and transfer performance. We release the checkpoints and the evaluation code at https://github.com/deepmind/semppl ., Comment: Published as a conference paper at ICLR 2023. For checkpoints and source code see https://github.com/google-deepmind/semppl

High-entropy order-disorder phase transitions can be used for efficient and eco-friendly barocaloric solid-state cooling. Here the barocaloric effect is reported in an archetypal plastic crystal, adamantane. Adamantane has a colossal isothermally reversible entropy change of 106 J K-1 kg-1 . Extremely low hysteresis means that this can be accessed at pressure differences less than 200 bar. Configurational entropy can only account for about 40% of the total entropy change; the remainder is due to vibrational effects. Using neutron spectroscopy and supercell lattice dynamics calculations, it is found that this vibrational entropy change is mainly caused by softening in the high-entropy phase of acoustic modes that correspond to molecular rotations. We attribute this behaviour to the contrast between an 'interlocked' state in the low-entropy phase and sphere-like behaviour in the high-entropy phase. Although adamantane is a simple van der Waals solid with near-spherical molecules, this approach can be leveraged for the design of more complex barocaloric molecular crystals. Moreover, this study shows that supercell lattice dynamics calculations can accurately map the effect of orientational disorder on the phonon spectrum, paving the way for studying the vibrational entropy, thermal conductivity, and other thermodynamic effects in more complex materials., Comment: 14 pages, 6 figures

We present the results of muon spin relaxation ($\mu$SR) and neutron scattering on the Ce-based quasikagome lattice CeRh$_{1-x}$Pd$_{x}$Sn ($x=0.1$ to 0.75). Our ZF-$\mu$SR results reveal the absence of static long-range magnetic order down to 0.05~K in $x = 0.1$ single crystals. The weak temperature-dependent plateaus of the dynamic spin fluctuations below 0.2~K in ZF-$\mu$SR together with its longitudinal-field (LF) dependence between 0 and 3~kG indicate the presence of dynamic spin fluctuations persisting even at $T$ = 0.05~K without static magnetic order. On the other hand, $C_{\text{4f}}$/$T$ increases as --log $T$ on cooling below 0.9~K, passes through a broad maximum at 0.13~K and slightly decreases on further cooling. The ac-susceptibility also exhibits a frequency independent broad peak at 0.16~K, which is prominent with an applied field $H$ along $c$-direction. We, therefore, argue that such a behavior for $x=0.1$ (namely, a plateau in spin relaxation rate ($\lambda$) below 0.2~K and a linear $T$ dependence in $C_{\text{4f}}$ below 0.13~K) can be attributed to a metallic spin-liquid (SL) ground state near the quantum critical point in the frustrated Kondo lattice. The LF-$\mu$SR study suggests that the out of kagome plane spin fluctuations are responsible for the SL behavior. Low energy inelastic neutron scattering (INS) of $x$ = 0.1 reveals gapless magnetic excitations, which are also supported by the behavior of $C_{\text{4f}}$ proportional to $T^{1.1}$ down to 0.06~K., Comment: 19 pages and 9 figures

A quantum spin liquid (QSL) is an exotic state in which electron spins are highly entangled, yet keep fluctuating even at zero temperature. Experimental realization of model QSLs has been challenging due to imperfections, such as antisite disorder, strain, and extra or a lack of interactions in real materials compared to the model Hamiltonian. Here we report the magnetic susceptibility, thermodynamic, inelastic neutron scattering (INS), and muon-spin relaxation studies on a polycrystalline sample of PrZnAl$_{11}$O$_{19}$, where the Pr$^{3+}$ ions form an ideal two-dimensional triangular lattice. Our results demonstrate that this system does not order nor freeze, but keep fluctuating down to 50 mK despite large antiferromagnetic couplings ($\sim$ -10 K). Furthermore, the INS and specific-heat data suggest that PrZnAl$_{11}$O$_{19}$ is best described as a gapless QSL., Comment: 8 pages, 6 figures

The structurally simplest high-temperature superconductor FeSe exhibits an intriguing superconducting nematic paramagnetic phase with unusual spin excitation spectra that are different from typical spin waves; thus, determining its effective magnetic exchange interactions is challenging. Here we report neutron scattering measurements of spin fluctuations of FeSe in the tetragonal paramagnetic phase. We show that the equal-time magnetic structure factor, $\mathcal{S}(\textbf{Q})$, can be effectively modeled using the self-consistent Gaussian approximation calculation with highly frustrated nearest-neighbor ($J_{1}$) and next-nearest-neighbor ($J_{2}$) exchange couplings, and very weak further neighbor exchange interaction. Our results elucidate the frustrated magnetism in FeSe, which provides a natural explanation for the highly tunable superconductivity and nematicity in FeSe and related materials.

Detailed thermodynamic analysis of complex systems with multiple stable configurational states allows for insight into the cooperativity of each individual transition. In this work we derive a heat capacity decomposition comprising contributions from each individual configurational state, which together sum to a baseline heat capacity, and contributions from each state-to-state transition. We apply this analysis framework to a series of replica exchange molecular dynamics simulations of linear and 1-1 coarse-grained homo-oligomer models which fold into stable, configurationally well-defined molecular knots, in order to better understand the parameters leading to stable and cooperative folding of these knots. We find that a stiff harmonic backbone bending angle potential is key to achieving knots with specific 3D structures. Tuning the backbone equilibrium angle in small increments yields a variety of knot topologies, including $3_1$, $5_1$, $7_1$, and $8_{19}$ types. Populations of different knotted states as functions of temperature can also be manipulated by tuning backbone torsion stiffness or by adding side chain beads. We find that sharp total heat capacity peaks for the homo-oligomer knots are largely due to a coil-to-globule transition, rather than a cooperative knotting step. However, in some cases the cooperativity of globule-to-knot and coil-to-globule transitions are comparable, suggesting that highly cooperative folding to knotted structures can be achieved by refining the model parameters or adding sequence specificity., Comment: 39 pages, 14 figures + supporting information

We report the synthesis of powder and single-crystal samples of the cerium pyrohafnate and their characterization using neutron diffraction, thermogravimetry and X-ray absorption spectroscopy. We evaluate the amount of non-magnetic Ce$^{4+}$ defects and use this result to interpret the spectrum of crystal-electric field transitions observed using inelastic neutron scattering. The analysis of these single-ion transitions indicates the dipole-octupole nature of the ground state doublet and a significant degree of spin-lattice coupling. The single-ion properties calculated from the crystal-electric field parameters obtained spectroscopically are in good agreement with bulk magnetic susceptibility data down to about 1 K. Below this temperature, the behavior of the magnetic susceptibility indicates a correlated regime without showing any sign of magnetic long-range order or freezing down to 0.08 K. We conclude that Ce$_2$Hf$_2$O$_{7}$ is another candidate to investigate exotic correlated states of quantum matter such as the octupolar quantum spin ice recently argued to exist in the isostructural compounds Ce$_2$Sn$_2$O$_7$ and Ce$_2$Zr$_2$O$_7$.

Ammonium sulfate is a giant inverse barocaloric material that is cheaply and commercially available. Exploiting its potential for cooling applications requires an understanding of the mechanism driving the entropy change. Here we report an investigation by inelastic neutron scattering and density functional theory of the phonons under working conditions of temperature and pressure. We find excellent agreement between the experimental and calculated results. The ammonium librational modes that are crucial to the entropy change are identifiable by their negative Gr\"{u}neisen parameter. Our results connect the differences in structure across the phase transition to those in the atomic dynamics, suggesting a route towards designing new caloric materials., Comment: This manuscript has now been incorporated into 2201.02548. Please check there for the latest version