588 Search Results

Supercritical fluids are characterized by unique thermodynamic properties. One of these properties is the existence of two-component dynamics that is associated with distinct low-frequency and high-frequency vibrational responses of the fluid. However, the origin of this behavior remains unknown. By combining inelastic X-ray scattering and molecular dynamics simulations, we show that this behavior can be connected to density heterogeneities arising from molecular clusters. Analyses of measurements and molecular trajectories suggest that the two-component dynamics emerges due to distinct momentum fluctuations of clustered and unbound molecules. This connection between clusters and two-component dynamics highlights the importance of molecular-structural heterogeneities in supercriticalmore » fluids, colloids, and condensed-matter systems.« less

We present results from co-designed quantum simulations of the neutrinoless double-β decay of a simple nucleus in 1+1D quantum chromodynamics using IonQ’s Forte-generation trapped-ion quantum computers. Electrons, neutrinos, and up and down quarks are distributed across two lattice sites and mapped to 32 qubits, with an additional 4 qubits used for flag-based error mitigation. A four-fermion interaction is used to implement weak interactions, and lepton-number violation is induced by a neutrino Majorana mass. Quantum circuits that prepare the initial nucleus and time evolve with the Hamiltonian containing the strong and weak interactions are executed on IonQ Forte Enterprise. Enabled bymore » tuned model parameters, lepton-number violation is observed in real time, providing a clear signal of neutrinoless double-β decay. This was made possible by co-designing the simulation to maximally utilize the all-to-all connectivity and native gate-set available on IonQ’s quantum computers. Quantum circuit compilation techniques and co-designed error-mitigation methods, informed from executing benchmarking circuits with up to 2,356 two-qubit gates, enabled observables to be extracted with high precision. We discuss the potential of future quantum simulations to provide yocto-second resolution of the reaction pathways in these, and other, nuclear processes.« less

The growing demand for rare earth elements (REEs) has prompted interest in their recovery from alternative sources such as coal fly ash (CFA). This study explores the ionic liquid (IL) betainium bis(trifluoromethylsulfonyl)imide, [Hbet][Tf₂N], for selective extraction of REEs from leachates of a Class C CFA. While previous studies have demonstrated the effectiveness of using [Hbet][Tf₂N] to extract REEs from different types of CFA in direct ash-IL systems, this study investigates four CFA leachates prepared using HCl, HNO₃, H₂SO₄, and citrate. Extraction experiments were conducted across varying pH levels and with additives such as ascorbic acid and betaine. Among the systemsmore » tested, [Hbet][Tf₂N] achieved REE recoveries of 51% and 47% from the HCl and citrate leachates, respectively, comparable to 49% REE recovery in ash-IL extraction. Co-extraction of bulk elements was significantly reduced in the leachate-IL systems. Optimal REE extraction occurred near pH 11, and addition of ascorbic acid effectively suppressed iron co-extraction without compromising REE recovery. Recycling experiments demonstrated that [Hbet][Tf₂N] retains its performance over five cycles with manageable losses. These results reveal the promise of [Hbet][Tf₂N] for effectively recovering REEs from leachates of solid wastes, highlighting its applicability as a sustainable strategy for other aqueous REE feedstocks.« less

Electronic ground states are of central importance in chemical simulations, but have remained beyond the reach of efficient classical algorithms except in cases of weak electron correlation or one-dimensional spatial geometry. We introduce a hybrid quantum-classical eigenvalue solver that constructs a wavefunction ansatz from a linear combination of matrix product states in rotated orbital bases, enabling the characterization of strongly correlated ground states with arbitrary spatial geometry. The energy is converged via a gradient-free generalized sweep algorithm based on quantum subspace diagonalization, with a potentially exponential speedup in the off-diagonal matrix element contractions upon translation into compact quantum circuits ofmore » linear depth in the number of qubits. Chemical accuracy is attained in numerical experiments for both a stretched water molecule and an octahedral arrangement of hydrogen atoms, achieving substantially better correlation energies compared to a unitary coupled-cluster benchmark, with orders of magnitude reductions in quantum resource estimates and a surprisingly high tolerance to shot noise. This proof-of-concept study suggests a promising new avenue for scaling up simulations of strongly correlated chemical systems on near-term quantum hardware.« less

We describe a resonant cavity search apparatus for axion dark matter constructed by the quantum sensors for the hidden sector collaboration. The apparatus is configured to search for QCD axion dark matter, though also has the capability to detect axion-like particles, dark photons, and some other forms of wave-like dark matter. Initially, a tuneable cylindrical oxygen-free copper cavity is read out using a low noise microwave amplifier feeding a heterodyne receiver. The cavity is housed in a dilution refrigerator (DF) and threaded by a solenoidal magnetic field, nominally 8 T. The apparatus also houses a magnetic field shield for housingmore » superconducting electronics, and several other fixed-frequency resonators for use in testing and commissioning various prototype quantum electronic devices sensitive at a range of axion masses in the range 2.0–40 μeV c^-2. The apparatus as currently configured is intended as a test stand for electronics over the relatively wide frequency band attainable with TM₀₁₀ the cavity mode used for axion searches. We present performance data for the resonator, DF, and magnet, and plans for the first science run.« less

We employed cryogenic X-ray Raman spectroscopy to investigate the early-stage decomposition of the high explosive molecule hexanitrohexaazaisowurtzitane (CL-20). By systematically varying the radiation dose under cryogenic conditions, we induced the decomposition of the molecule using ionizing radiation and observed the evolution of spectral features at the carbon, nitrogen, and oxygen K edges. Through extensive first-principles calculations, we identified key intermediates in the early stages of the decomposition process, resulting from C–C and C–N bond cleavage which leads to the opening of the internal cage structure. A detailed analysis of spectral trends and fingerprints provided evidence supporting N–NO₂ homolytic cleavage asmore » the primary initial decomposition pathway. The combination of advanced core-level spectroscopy methods and state-of-the-art theoretical calculations enabled a comprehensive characterization of the molecular changes induced by controlled radiation dose exposures. In conclusion, our findings establish a benchmark for understanding the decomposition chemistry of high-explosive materials, offering important insights into their stability and reactivity under extreme conditions.« less

Solid-solid phase change materials (SS-PCMs) hold promise for energy storage/dissipation in batteries and energetic materials. Yet, phase change kinetics for SS-PCMs undergoing metastable to semi-stable/stable phase transformations remain relatively ill-studied because trapping metastable phases remain challenging. Recently, we demonstrated the kinetic entrapment and stabilization of a highly disordered and amorphous Al-oxide phase m-AlO_x@C (x~2.5-3.0) via laser ablation synthesis in solution (LASiS). We report here, to our knowledge, the first chemical kinetics analysis for S-S phase transition of the m-AlO₃@C nanocomposites (< 5–8 nm sizes) into semi-stable equilibrium alumina phases (θ/γ-Al₂O₃) via disproportionation reaction, while releasing excess trapped gases. Our results indicatemore » the atomic density of the AlO₃ structures to be ~5–10 times less than that of the final Al₂O₃ phases, which led to the hypothesis of a volume shrinkage process during their phase transition. Temperature-dependent X-ray diffraction studies reveal the high-temperature phase transition for m-AlO₃ → θ/γ-Al₂O₃ to follow contracting volume kinetics model, thereby validating our earlier hypothesis. Using the geometric volume contraction model, reaction kinetics analyses from Arrhenius plots reveal the activation energy barrier for the phase transition to be ~270±11 kJ/mol. This makes the activation energy barrier nearly identical to the oxidation of micron-sized Al particles.« less

Shock-induced detonation of insensitive high explosives based on 1,3,5-triamino-2,4,6-trinitrobenzene starts with formation of hot spots at microstructural defects but has eluded atomistic modeling treatment at micron length scales. To this end, we performed multimicron scale all-atom molecular dynamics (MD) simulations of hot spots that form during the collapse of cylindrical pores with diameters between 10 and 300 nm. Our MD simulations show that hot spots formed at pores larger than 20 nm exhibit temperature fields with scale-invariant features for sizes up to at least 300 nm. Through a continuum-based grain-scale modeling framework, we span and extend beyond the size scalesmore » currently accessible to MD and find that hot spot scale invariance is a general feature that arises when the mechanical strength is insensitive to strain rate. Finally, our results demonstrate the applicability of all-atom MD to simulate the complicated dynamical evolution of micron-sized systems and bolster confidence in insights from MD simulations of materials that exhibit strength with negligible rate dependence over the relevant intervals.« less

Neutrinos are unique among elementary particles in that their flavor-compositions oscillate over time. In extreme environments such as core-collapse supernovae, neutron-star mergers, and the early Universe, neutrinos are dense enough that their self-interactions significantly affect, if not dominate, these oscillations. This has implications for several phenomena within these environments, particularly nucleosynthesis. Simulations of these self-interactions have traditionally approximated neutrinos as having two flavors instead of the physical three. In order to develop techniques for characterizing the resulting quantum entanglement, I present the results of simulations of neutrino-neutrino interactions that include all three physical neutrino flavors and were performed on D-Wavemore » Inc.’s Advantage 5000+ qubit quantum annealer. These results are checked against those from exact classical simulations, which are also used to compare the neutrino-neutrino interactions to neutrino-antineutrino and interactions between Majorana neutrinos, which are their own antiparticles. The D-Wave Advantage annealer is shown to be able to reproduce time evolution with the precision of a classical machine for small numbers of neutrinos and to do so without the Trotter errors present in most simulations of dynamics on quantum devices. Furthermore, it suffers from poor scaling in qubit-count with the number of neutrinos.« less

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet),more » MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.« less