Searching for superconductivity in high entropy oxide Ruddlesden?Popper cuprate films
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Nanomaterials have revolutionized the battery industry by enhancing energy storage capacities and charging speeds, and their application in hydrogen (H 2 ) storage likewise holds strong potential, though with distinct challenges and mechanisms. H 2 is a crucial future zero-carbon energy vector given its high gravimetric energy density, which far exceeds that of liquid hydrocarbons. However, its low volumetric energy density in gaseous form currently requires storage under high pressure or at low temperature. This review critically examines the current and prospective landscapes of solid-state H 2 storage technologies, with a focus on pragmatic integration of advanced materials such as metal-organic frameworks (MOFs), magnesium-based hybrids, and novel sorbents into future energy networks. These materials, enhanced by nanotechnology, could significantly improve the efficiency and capacity of H 2 storage systems by optimizing H 2 adsorption at the nanoscale and improving the kinetics of H 2 uptake and release. We discuss various H 2 storage mechanisms—physisorption, chemisorption, and the Kubas interaction—analyzing their impact on the energy efficiency and scalability of storage solutions. The review also addresses the potential of “smart MOFs”, single-atom catalyst-doped metal hydrides, MXenes and entropy-driven alloys to enhance the performance and broaden the application range of H 2 storage systems, stressing the need for innovative materials and system integration to satisfy future energy demands. High-throughput screening, combined with machine learning algorithms, is noted as a promising approach to identify patterns and predict the behavior of novel materials under various conditions, significantly reducing the time and cost associated with experimental trials. In closing, we discuss the increasing involvement of various companies in solid-state H 2 storage, particularly in prototype vehicles, from a techno-economic perspective. In conclusion, this forward-looking perspective underscores the necessity for ongoing material innovation and system optimization to meet the stringent energy demands and ambitious sustainability targets increasingly in demand.
We investigate the phase behavior of ternary mixtures of ionic liquid, organic solvent, and lithium salt by molecular dynamics simulations. We find that at room temperature, the electrolyte separates into distinct phases with specific compositions; an ion-rich domain that contains a fraction of solvent molecules and a second domain of pure solvent. Here, the phase separation is shown to be entropy-driven and is independent of lithium salt concentration. Phase separation is only observed at microsecond time scales and greatly affects the transport properties of the electrolyte.
Ni 40 Co 20 Fe 10 Cr 10 Al 18 W 2 additively manufactured using laser powder bed fusion (LPBF) is among the toughest as-built alloys reported and is a promising candidate for use in extreme environments. However, its behavior under multi-megabar pressure regimes remains unexplored. We used femtosecond in situ X-ray diffraction to investigate the shock response of LPBF Ni40Co20Fe10Cr10Al18W2 under laser-driven shock compression and release. Our results reveal that the initial dual-phase face-centered + body-centered structure transforms to a single face-centered phase over a wide pressure range of 84±10 to 277±55 GPa, followed by a transition to a single body-centered phase at 431±48 GPa. We establish the Hugoniot equation-of-state of LPBF Ni 40 Co 20 Fe 10 Cr 10 Al 18 W 2 and compare it to the benchmark alloy AlCoCrFeNi 2.1 , demonstrating the effects of W-doping and increased Al content. High stacking fault probabilities, close to those measured in Au and Ag, are observed upon compression. A portion of the stacking faults are annihilated upon release to ambient pressure.
Bioinspired hierarchical microstructures offer a route toward engineered fatigue resistance in additively manufactured alloys. However, it remains unclear how discrete structural constituents independently govern damage accumulation, particularly during the critical fatigue initiation regime where short cracks strongly interact with local microstructure. Here, we investigate multiscale fatigue initiation in a dual-phase, nanolamellar AlCoCrFeNi 2.1 high-entropy alloy. By comparing microscale specimens that isolate the nanolamellar structure against macroscale specimens containing the full melt-pool architecture, we identify size-dependent fatigue initiation mechanisms. We find that failure is dictated by nanolamellar interfaces at the microscale, whereas mesoscale melt pool boundaries serve to initiate fatigue at the macroscale. This mechanistic shift is accompanied by a transition from macroscale quasi-brittle failure to microscale plasticity-driven crack extension. Our results provide a physical framework for understanding how structural hierarchy governs the transition from discrete microstructural deformation to continuum fatigue fracture behavior, informing the design of damage-tolerant, additively manufactured alloys.
Abstract Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently outperform physics-based models for systems with few observable degrees of freedom; e.g. hydrological systems. Here, we provide an operator-theoretic explanation for this empirical success. To predict a partially-observed system’s future behavior with physics-based models, the missing degrees of freedom must be explicitly accounted for using data assimilation and model parametrization. Data-driven models, in contrast, employ delay-coordinate embeddings and their evolution under the Koopman operator to implicitly model the effects of the missing degrees of freedom. We describe in detail the statistical physics of partial observations underlying data-driven models using novel maximum entropy and maximum caliber measures. The resulting nonequilibrium Wiener projections applied to the Mori–Zwanzig formalism reveal how data-driven models may converge to the true dynamics of the observable degrees of freedom. Additionally, this framework shows how data-driven models infer the effects of unobserved degrees of freedom implicitly, in much the same way that physics models infer the effects explicitly. This provides a unified implicit-explicit modeling framework for predicting partially-observed systems, with hybrid physics-informed machine learning methods combining both implicit and explicit aspects.
The coherence of collective modes, such as phonons and polarons, and their modulation of electronic states is long sought in complex systems, which is a crosscutting issue in photovoltaics and quantum electronics. In photovoltaic cells and lasers based on metal halide perovskites, the presence of polarons, i.e., photocarriers dressed by the macroscopic motion of charged lattice, assisted by terahertz (THz) longitudinal-optical (LO) phonons, has been intensely studied yet is still debated. This may be key for explaining the remarkable properties of the perovskite materials, e.g., defect tolerance, long charge lifetimes, and diffusion lengths. In this work we use the intense single-cycle THz pulse with peak electric field up to E THz = 1000 kV/cm to drive coherent polaronic band-edge oscillations at room temperature in CH 3 NH 3 PbI 3 (MAPbI 3 ). We reveal the oscillatory behavior is dominated by a specific quantized lattice vibration mode at $ω$ LO ~ 4 THz, which is both dipole and momentum forbidden. THz-driven coherent polaron dynamics exhibits distinguishing features: room temperature coherent oscillations at ω LO longer than 1 ps in both single crystals and thin films, mode-selective modulation of different band-edge states assisted by electron-phonon interaction, and dynamic mode splitting at low temperature due to entropy and anharmonicity of organic cations. Our results demonstrate intense THz-driven coherent band-edge modulation is a powerful probe of electron-lattice coupling phenomena and polaronic quantum control in perovskites.
Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.
We present an ab initio free energy model derived from a fully relativistic density functional theory (DFT) electronic structure with dynamic magnetism for δ -plutonium (face-centered cubic, fcc). The DFT model is extended with orbital-orbital interaction in a parameter free orbital polarization (OP) mechanism consistent with previous modeling of plutonium. Gibbs free energy is built from components associated with the temperature dependence of the electronic structure and the corresponding electronic entropy, lattice vibrations within an anharmonic lattice dynamics model, and dynamical fluctuations of the magnetization density, i.e. magnetic fluctuations. The fluctuation model consists of transverse and longitudinal modes driven by temperature induced excitations of the DFT + OP electronic structure. The ab initio model thus incorporates fluctuating states beyond the electronic ground state. Thanks to the dynamic magnetism, the theory predicts excellent thermodynamic properties and a Gibbs free energy in accord with CALPHAD and semi-empirical modeling developed from the thermodynamic observables. The magnetic fluctuations further explain anomalous behaviors of the thermal expansion in plutonium. Specifically, a thermal expansion for the δ -plutonium system turning from positive to negative at temperatures above room temperature, a tendency for gallium to reduce and remove the negative thermal expansion depending on composition, and a positive thermal expansion for the high temperature ϵ phase.
In this work, we report a detailed experimental study regarding the impact of copper oxide (CuO) secondary phase and heat treatments on the structural, magnetic, and magnetocaloric properties of the near-room temperature Pr 2/3 Sr 1/3 MnO 3 (PSMO) magnetic refrigerant. Our investigations are carried out by using structural and microstructural analyses, alongside magnetization measurements. Here, the analysis of X-ray diffraction data of PSMO(95%)-CuO(5%) (PSMO-CuO) samples shows the coexistence of both CuO and PSMO phases. Further, the microstructural analysis of PSMO-CuO reveals that the addition of CuO significantly enhances the grains size. On the other hand, the added secondary phase markedly reduces the Curie temperature (T c ) from 293 K for PSMO to about 273 K for PSMO-CuO composite while increasing the magnetocaloric effect. This decrease in T c is associated with a significant change from 162° to about 156° in the Mn-OII-Mn bond angle respectively. Moreover, performed investigations regarding the role of heat treatments unveil that the observed changes in the structural and magnetic features are mainly driven by the secondary phase that modify grains size and double-exchange interactions in the PSMO compound. Interestingly, our findings demonstrate that the Curie temperature of the PSMO and accordingly its magnetocaloric effect can be tailored by adding small amounts of CuO without need to substitution on cation sites. In the light of obtained results, a multilayered refrigerant composed of PSMO and PSMO-CuO is proposed to cover the magnetic cooling temperature range close to room-temperature. The resulting entropy change remains practically constant between 273 K and 293 K. Such a behavior is highly appreciated from a practical point of view, particularly in cases where the cooling process is carried out by using the AMR and Ericsson cycles.
Inspired by the Hayden-Preskill protocol for black hole evaporation, we consider the dynamics of a quantum many-body qudit system coupled to an external environment, where the time evolution is driven by the continuous limit of certain 2-local random unitary circuits. We study both cases where the unitaries are chosen with and without a conserved U(1) charge and focus on two aspects of the dynamics. First, we study analytically and numerically the growth of the entanglement entropy of the system, showing that two different time scales appear: one is intrinsic to the internal dynamics (the scrambling time), while the other depends on the system-environment coupling. In the presence of a U(1) conserved charge, we show that the entanglement follows a Page-like behavior in time: it begins to decrease in the middle stage of the “evaporation”, and decreases monotonically afterwards. Second, we study the time needed to retrieve information initially injected in the system from measurements on the environment qudits. Based on explicit numerical computations, we characterize such time both when the retriever has control over the initial configuration or not, showing that different scales appear in the two cases.
Fivefold and icosahedral symmetries induced by multiply twinned crystal structures have been studied extensively for their role in influencing the shape of synthetic nanoparticles, and solution chemistry or geometric confinement are widely considered to be essential. Here we report the purely entropy-driven formation of fivefold and icosahedral twinned clusters of particles in molecular simulation without geometric confinement or chemistry. Hard truncated tetrahedra self-assemble into cubic or hexagonal diamond colloidal crystals depending on the amount of edge and vertex truncation. By engineering particle shape to achieve a negligible entropy difference between the two diamond phases, we show that the formation of the multiply twinned clusters is easily induced. The twinned clusters are entropically stabilized within a dense fluid by a strong fluid-crystal interfacial tension arising from strong entropic bonding. Our findings provide a strategy for engineering twinning behavior in colloidal systems with and without explicit bonding elements between particles.
In high-energy astrophysical processes involving compact objects, such as core-collapse supernovae or binary neutron star mergers, neutrinos play an important role in the synthesis of nuclides. Neutrinos in these environments can experience collective flavor oscillations driven by neutrino–neutrino interactions, including coherent forward scattering and incoherent (collisional) effects. Recently, there has been interest in exploring potential novel behaviors in collective oscillations of neutrinos by going beyond the one-particle effective or "mean-field" treatments. Here, we seek to explore implications of collective neutrino oscillations, in the mean-field treatment and beyond, for the nucleosynthesis yields in supernova environments with different astrophysical conditions and neutrino inputs. We find that collective oscillations can impact the operation of the νp-process and r-process nucleosynthesis in supernovae. The potential impact is particularly strong in high-entropy, proton-rich conditions, where we find that neutrino interactions can nudge an initial νp-process neutron-rich, resulting in a unique combination of proton-rich low-mass nuclei as well as neutron-rich high-mass nuclei. We describe this neutrino-induced neutron-capture process as the "νi-process." In addition, nontrivial quantum correlations among neutrinos, if present significantly, could lead to different nuclide yields compared to the corresponding mean-field oscillation treatments, by virtue of modifying the evolution of the relevant one-body neutrino observables.
We present a systematic investigation of thermodynamic stability, phase-reaction, and chemical activity of Al containing disordered Ti 2 (Al-Ga)C MAX phases using machine-learning driven high-throughput framework to understand the oxidation resistance behavior with increasing temperature and exposure to static oxygen. The A-site (at Al) disordering of Ti 2 AlC with Ga shows significant change in the chemical activity of Al with increasing temperature and exposure to static oxygen, which is expected to enable surface segregation of Al, thereby, the formation of Al 2 O 3 and improved oxidation resistance. We performed in-depth convex hull analysis of ternary Ti-Al-C, Ti-Ga-C, and Ti-Al-Ga-C based MAX phase, and provide detailed contribution arising from electronic, chemical and vibrational entropies. The thermodynamic analysis shows change in the Gibbs formation enthalpy (ΔG form ) at higher temperatures, which implies an interplay of temperature-dependent enthalpy and entropic contributions in oxidation resistance Ga doped Ti 2 AlC MAX phases. A detailed electronic structure and chemical bonding analysis using crystal orbital Hamilton population method reveal the origin of change in phases stability and in oxidation resistance in disorder Ti2(Al 1-x Ga x )C MAX phases. Our electronic structure analysis correlate well with the change in oxidation resistance of Ga doped MAX phases. We believe our study provides a useful guideline to understand to role of alloying on electronic, thermodynamic, and oxidation related mechanisms of bulk MAX phases, which can work as a precursor to understand oxidation behavior of twodimensional MAX phases, i.e., MXenes (transition metal carbides, carbonitrides and nitrides).
Refractory multi-principal element alloys (RMPEAs) combine a chemically disordered lattice with a structurally ordered, single‐phase body‐centered cubic (bcc) crystal structure. Chemical fluctuations in these alloys give rise to significant energy barriers that impede dislocation motion. In this study, we use phase field dislocation dynamics to examine how spatial temperature fluctuations compete with randomness in energy barriers to affect the motion of long screw dislocations in three equi-atomic MoNbTa‐based RMPEAs: MoNbTa, MoNbTaW, and MoNbTaVW. Over a wide range of homologous temperatures (T h ≈ 0–0.6), we determined a screw dislocation ‘flow stress’ as the minimum applied stress to sustain continuous motion over a long excursion distance within a fixed timeframe. All three RMPEAs exhibit temperature dependent flow stress with three characteristic glide regimes: at low homologous temperatures, flow stress drops sharply, and screw glide remains planar and rectilinear; at intermediate homologous temperatures, the flow stress levels off as glide becomes planar but wavy; and at high homologous temperatures, flow stress plateaus as screws exhibit nonplanar, three‐dimensional motion. Glide kinetics and transition temperatures are controlled by the chemically induced fluctuations in the energy landscape. The rectilinear to wavy transition temperature is controlled by statistically weakest local barriers in the glide plane, whereas the wavy to 3D transition temperature is governed by statistically strongest local barriers. At low and intermediate homologous temperatures, the relative spread in energy barriers governs glide behavior by controlling the local kinetics of kink pair formation and kink pinning. At high homologous temperatures, the average barrier height governs the glide behavior by controlling the number of out-of-plane excursions during 3D glide. These findings reveal how random chemical fluctuations determine screw‐driven plasticity in RMPEAs, providing critical insight for the design of high temperature structural alloys.
The search for novel entangled phases of matter has lead to the recent discovery of a new class of “en- tanglement transitions,” exemplified by random tensor networks and monitored quantum circuits. Most known examples can be understood as some classical ordering transitions in an underlying statistical mechanics model, where entanglement maps onto the free-energy cost of inserting a domain wall. In this paper we study the possibility of entanglement transitions driven by physics beyond such statistical mechanics mappings. Motivated by recent applications of neural-network-inspired variational Ansätze, we investigate under what conditions on the variational parameters these Ansätze can capture an entanglement transition. We study the entanglement scaling of short-range restricted Boltzmann machine (RBM) quantum states with random phases. For uncorrelated random phases, we analytically demonstrate the absence of an entanglement transition and reveal subtle finite-size effects in finite-size numerical simulations. Introducing phases with correlations decaying as 1/r α in real space, we observe three regions with a different scaling of entanglement entropy depending on the exponent α. We study the nature of the transition between these regions, finding numerical evidence for critical behavior. Furthermore, our work establishes the presence of long-range correlated phases in RBM-based wave functions as a required ingredient for entanglement transitions.
The adsorption of ions to water-hydrophobe interfaces influences a wide range of phenomena, including chemical reaction rates, ion transport across biological membranes, and electrochemical and many catalytic processes; hence, developing a detailed understanding of the behavior of ions at water-hydrophobe interfaces is of central interest. Here, we characterize the adsorption of the chaotropic thiocyanate anion (SCN − ) to two prototypical liquid hydrophobic surfaces, water-toluene and water-decane, by surface-sensitive nonlinear spectroscopy and compare the results against our previous studies of SCN − adsorption to the air-water interface. For these systems, we observe no spectral shift in the charge transfer to solvent spectrum of SCN − , and the Gibb’s free energies of adsorption for these three different interfaces all agree within error. We employed molecular dynamics simulations to develop a molecular-level understanding of the adsorption mechanism and found that the adsorption for SCN − to both water-toluene and water-decane interfaces is driven by an increase in entropy, with very little enthalpic contribution. This is a qualitatively different mechanism than reported for SCN − adsorption to the air-water and graphene-water interfaces, wherein a favorable enthalpy change was the main driving force, against an unfavorable entropy change.
High-entropy oxides (HEOs) have unveiled a unique frontier in the realm of heterogeneous catalysis, taking advantage of the entropic effect and increased complexities to deliver ultrahigh stability and large tuning capability. However, current HEO synthesis mainly relies on high-temperature annealing approaches affording HEOs possessing no or low surface area, inferior active site exposure efficiency, and low controllability over the structure tuning. The grand challenge lies in producing high-quality HEO catalysts with high active site utilization efficiency, which relies on precision structure engineering, preferably under mild conditions. In this work, an in situ lattice engineering approach was developed to afford a supported HEO catalyst under ambient conditions. The HEO compositions (CuCoFeNiMnO x ) were uniformly integrated into the lattice of CeO 2 driven by cavitation-induced nucleation being generated via ultrasonication. The as-afforded catalysts were featured by high surface area, atomically dispersed HEO compositions, active redox properties, abundant oxygen vacancies (O V ), antiagglomeration, and high phase stability under harsh conditions. Compared with the ex situ introduction of HEO on the surface, the in situ method provides dual benefits to maintain the dispersity of HEO via entropic and lattice confinement effects. Engineering the complex HEO within the lattice of fluorite-structured CeO 2 also yields abundant defects (e.g., O V ) and active metal sites with strong reducing properties (e.g., Ce 3+ and Cu + ), which greatly improves the activity of the lattice oxygen and tunability of the adsorption behavior of the guest molecules, especially in the presence of impurities (e.g., water and propane). The catalytic performance of the supported HEO catalyst in oxidative procedures surpasses the pure dense phase HEO as well as the ex situ-generated catalysts. Further, the synthesis approach being developed in this work, together with the fundamental understanding in structure evolution and reaction mechanism, showcases a facile pathway under ambient conditions to generate stable catalysts capable of maintaining structural robustness in high-temperature conditions while delivering enhanced catalytic performance.