Related Articles
Prediction of thermal conductivity in CALF-20 with first-principles accuracy via machine learning interatomic potentials
Understanding the thermal transport properties of CALF-20, a recent addition to the metal-organic framework family, is crucial for its effective utilization in greenhouse gas capture. Here, we report the thermal transport study of CALF-20 using artificial neural network-based machine learning potentials. We use the Green-Kubo approach based on equilibrium molecular dynamics, with a heat-flux renormalization technique, to determine the thermal conductivity (κ) of CALF-20. We predict that the anisotropic thermal transport in CALF-20, with κ below 1 Wm−1K−1 at 300 K, is ideal for thermoelectric applications. Our analysis reveals a weak temperature dependence (κ ~ 1/T0.56) and near invariance with pressure in κ value of CALF-20, which stands out from the typical trend observed in crystalline materials. The outcome of the study, leveraging advanced computational techniques for predictive modeling, offers valuable insights into more suitable applications of CALF-20 with tailored thermal properties.
Suppressed ballistic transport of dislocations at strain rates up to 109 s–1 in a stable nanocrystalline alloy
Dislocations are crucial to plastic deformation in crystals. At extreme strain rates, their motion shifts from thermally activated glide to ballistic transport, causing significant drag due to interactions with phonons, which can lead to embrittlement and failure in metals. The concept of dislons, quantized dislocations, has emerged to better understand these types of interactions. Similar to quantum treatment of dislocation-electron interactions, confining dislocations to nanometer scales, especially in nanocrystalline metals, could also yield unique mechanical behaviors different from bulk materials. Here, we present evidence showing that in Cu-3Ta, a thermo-mechanically stable nanocrystalline alloy, the phonon drag effect is entirely suppressed even at ultra-high strain rates (109 s−1). This is due to the stable confinement of dislocations within several-nanometer range, limiting their velocity and interaction with phonons. Our study indicates that in confined environments, the dislocation-phonon drag effect is minimal, potentially improving material performance under extreme conditions.
Ultrafast pump-probe phase-randomized tomography
Measuring fluctuations in matter’s low-energy excitations is the key to unveiling the nature of the non-equilibrium response of materials. A promising outlook in this respect is offered by spectroscopic methods that address matter fluctuations by exploiting the statistical nature of light-matter interactions with weak few-photon probes. Here we report the first implementation of ultrafast phase randomized tomography, combining pump-probe experiments with quantum optical state tomography, to measure the ultrafast non-equilibrium dynamics in complex materials. Our approach utilizes a time-resolved multimode heterodyne detection scheme with phase-randomized coherent ultrashort laser pulses, overcoming the limitations of phase-stable configurations and enabling a robust reconstruction of the statistical distribution of phase-averaged optical observables. This methodology is validated by measuring the coherent phonon response in α-quartz. By tracking the dynamics of the shot-noise limited photon number distribution of few-photon probes with ultrafast resolution, our results set an upper limit to the non-classical features of phononic state in α-quartz and provide a pathway to access non-equilibrium quantum fluctuations in more complex quantum materials.
Enhanced superconductivity near a pressure-induced quantum critical point of strongly coupled charge density wave order in 2H-Pd0.05TaSe2
Interplay between charge density wave (CDW) order and superconductivity (SC) in quasi-two-dimensional materials remains poorly understood due to their diverse experimental varieties. Here, we investigate the pressure-dependent electrical transport and Raman scattering spectra of 2H-Pd0.05TaSe2, which exhibits a CDW transition at TCDW = 115 K and a superconducting transition at Tc = 2.6 K at ambient pressure conditions. As pressure increases, TCDW, identified by the resistivity anomaly, shifts towards lower temperatures and approaches zero at a critical pressure of Pc ~ 21.5 GPa. At this critical pressure, both Tc and upper critical field Hc2 reach their maximum values of ~ 8.5 K and ~ 6.4 T, respectively. Analysis of the Raman scattering spectra demonstrates that increasing pressure systematically suppresses both the two-phonon spectral weight above TCDW and the CDW amplitudon energies below TCDW, leading to their simultaneous disappearance at Pc. These observations provide direct evidence for the formation of a CDW quantum critical point (QCP) at Pc, indicating that charge and lattice fluctuations associated with the QCP of strongly coupled CDW order may enhance SC in pressurized 2H-Pd0.05TaSe2.
First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes
MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.
Responses