Fast and reversible ion transport is a key barrier in advancing aqueous zinc-ion batteries (ZIBs). Here, we engineer MoS2 with intercalated sodium alginate (SA) to create a proton-conductive, expanded-layer architecture (0.96 nm) that enables efficient co-intercalation of hydrated Zn2+ and H+ ions. The SA network promotes Grotthuss-type proton hopping, while electronic-structure analysis reveals that proton-induced electrostatic screening flattens the Zn2+ diffusion landscape. MoS2/SA delivers high capacity (301.6 mAh/g) and fast diffusion (10-5 to 10-7cm2/s), supported by in-situ and operando Raman, in-situ electrochemical impedance spectroscopy, operando pH, and ex-situ X-ray photoelectron spectroscopy. Our machine learning (ML) model trained on experimental and DFT descriptors accurately predicts current response and deciphers feature importance, linking H+ content and diffusion barriers to enhanced kinetics. Statistical time-series analysis of GCD data using lag-1 autocorrelation and decay constant (tau) metrics confirms MoS2/SA's fastest electrochemical relaxation across current densities. A flexible quasi-solid-state ZIB further demonstrates stable operation and minimal self-discharge (79.2% capacity retention after 72 h), underscoring the promise of polymer-assisted, DFT and ML-guided cathode design.

Identifying physically meaningful structural and functional similarities across chemically and compositionally diverse materials remains a central challenge in data-driven materials science. Here we introduce MagnoCluster, a physics-informed unsupervised learning framework for interpretable materials classification, demonstrated across 515 lanthanide- and transition-metal-based magnetocaloric compounds. The framework integrates centroid-based and hierarchical clustering algorithms with multi-scale descriptor construction and adaptive feature scaling strategies, operating as a physics-guided similarity discovery engine rather than a conventional property predictor. Applied to the magnetocaloric materials space, MagnoCluster reveals latent material families governed by correlated charge-transfer behavior, local coordination motifs, lattice packing and molar volume, electronic-structure fingerprints, and thermodynamic stability—rather than nominal chemical composition alone. A central finding is that magnetocaloric performance is organized not by chemistry or crystal structure, but by a latent competition among electronic instability, magnetoelastic coupling, and intrinsic entropy capacity. Quantitative validation using silhouette analysis, Davies-Bouldin and Calinski-Harabasz indices, and low-dimensional manifold embeddings demonstrates that the resulting clusters encode robust and interpretable structure-property relationships. To validate the predictive capacity of the framework, a rare-earth intermetallic compound, Pr1.7Nd0.3In, an intermediate composition of recently discovered cryogenic magnetocaloric material series Pr2-xNdxIn, was synthesized, and its magnetocaloric properties were experimentally studied, yielding the maximum magnetic field induced entropy change of 24 J kg-1 K-1 in close agreement with Random Forest and XGBoost model predictions. This experimental confirmation establishes MagnoCluster as a reliable bridge between data-driven pattern discovery and targeted materials synthesis, providing a physically grounded taxonomy for interpretable classification and extrapolative design of next-generation magnetocaloric and other functional materials.

Tungsten-based multi-principal element alloys (W-MPEAs) are promising candidates for extreme-environment applications, yet their adoption in laser powder bed fusion (L-PBF) additive manufacturing remains limited by intrinsic brittleness and narrow processing windows. We present a unified computational framework coupling electronic-structure-derived ductility descriptors with physics-based L-PBF printability assessment to co-optimize composition and processing parameters across the W-Ti-V, W-Ti-Zr, and W-V-Zr ternary systems. Multi-objective Pareto analysis reveals a compact, well-defined design window where thermodynamic stability, intrinsic ductility, and solidification-crack resistance are simultaneously satisfied, demonstrating that compositional adjustments suppressing brittle fracture also reduce crack initiation during L-PBF thermal cycling. W-Ti-V, emerges as the optimal system, with the selected alloy retaining a broad L-PBF success domain while shifting defect boundaries relative to pure W, expanding the viable operating window to 0.5-2.2 m/s and 300-900 W. This framework establishes a physically consistent, experimentally actionable route for discovering printable, damage-tolerant W-MPEAs.

The confluence of strong electronic correlations and Berry curvature-driven transport constitutes a largely underexplored frontier in quantum materials research, particularly in systems where electronic correlations arise from flat-band physics rather than conventional f-electron states. Here, we report an intrinsic Berry curvature driven anomalous Hall transport in the dilute Kondo system Ru_2Mn_0.5Ti_0.5Ge, a non-f-electron Heusler alloy hosting flat bands with van Hove singularity proximate to the Fermi level. The anomalous Hall response is strikingly non-monotonic, violating the Fermi-liquid scaling relation near the magnetic transition and is restored only at low temperature, coincident with the onset of Kondo coherence. Ab initio calculations strongly establish that the Berry curvature originates from the Kondo hybridization induced interplay of flat bands and spin-orbit coupling mediated anticrossings at the Fermi level. Our findings demonstrate that Kondo coherence of flat bands bears a profound impact on Berry curvature associated ferroic responses, mandating a rigorous theoretical understanding of anomalous transport in the regime where reciprocal space topology and strong correlations are intrinsically intertwined.

Hydrogen (H) content modifies the creep response of Fe-based alloys by altering thermodynamics of point-defects; here we identify the electronic-structure mechanism underlying this effect. Using spin-polarized first-principles calculations combined with a cluster dynamics formulation, we establish a general framework linking H-assisted vacancy stabilization to diffusion-mediated creep in BCC Fe, FCC Fe, and chemically complex FCC Fe-Cr-Ni alloys. H-vacancy binding analysis shows that H-stabilized vacancies form at low hydrogen content in BCC Fe but require much higher chemical potentials in FCC Fe and Fe-Cr-Ni alloys due to broader d-bands, electronic screening, and chemical disorder. Consequently, plastic deformation mediated by diffusive processes is expected to be far more strongly impacted in BCC Fe than in FCC alloys. These electronic-controlled trends determine steady-state vacancy populations and provide a symmetry-resolved microscopic basis for H-assisted creep in ferritic and austenitic steels.

The development of aqueous zinc-ion batteries (AZIBs) is critically hindered by the absence of suitable cathode materials that simultaneously provide rapid Zn2+ diffusion kinetics and high energy density. We report a two-dimensional VO2/VS2 heterojunction nanobelt (VOSHN) as a superior cathode material enhancing specific capacity and longer cycle stability with improved Zn2+ diffusion kinetics. Further electronic features of the heterojunction and their diffusion behavior of Zn2+ in VOSHN are investigated by using DFT calculations. It emphasizes the lower Zn2+ diffusion barrier of 1.32 eV in VOSHN enabling high-rate performance in AZIBs. Consequently, the Zn//VO2/VS2 cell demonstrates a high specific capacity of 351 mAh g(-1) at 0.2 A g(-1) with capacity retention of 93.8% up to 3000 cycles at 3 A g(-1). Furthermore, the Zn//VO2/VS2 battery demonstrates a notable capacity of 44.89 mAh g(-1) with a retention of similar to 71% over 1500 cycles at a high current density of 5 A g(-1). The fascinating VOSHN offers synergistic effect delivering structural robustness, fast-charge transfer, and effective Zn2+ intercalation and hence improves the overall performance of aqueous Zn-ion batteries. This work provides novel routes for the design of 2D heterojunction cathode materials for practical AZIBs.

R2In (R = rare earth) intermetallics exhibit unusual magnetic and magnetocaloric properties, driven by subtle electronic effects, lattice distortions, and spin-lattice coupling. Most of these binary compounds adopt the hexagonal Ni2In-type structure at room temperature, with Eu2In and Yb2In stabilizing in the orthorhombic Co2Si-type lattice. Lighter lanthanide compounds Eu2In, Nd2In, and Pr2In undergo first-order magnetic transitions with negligible hysteresis and minimal lattice volume change and exhibit giant cryogenic magnetocaloric effects, while heavy lanthanide R2In compounds including Gd2In show second-order transitions with moderate magnetocaloric effect. No lanthanide-based R2In compound exhibits symmetry-breaking structural transition, while Y2In transforms from hexagonal to orthorhombic structure near 250 K. Secondary low-temperature transitions, including spin reorientation or antiferromagnetic ordering, further enrich the magnetic phase landscape in these compounds. Integrating theoretical descriptors such as charge-induced strain and electronic structure provides predictive insight into phase stability and magnetocaloric performance, guiding the design of rare-earth intermetallics with tunable magnetic properties for cryogenic applications

Achieving a balance among high magnetization, electrical resistivity, and mechanical formability remains a challenge in soft magnetic materials (SMMs). Conventional Fe-Co alloys deliver high magnetization but suffer from low electrical resistivity and limited ductility. Here, we demonstrate that engineering metastability in Fe-Co-Mn-Cr multi-principal-element alloys enables partial control of magnetic and mechanical properties via tailored phase competition amongst bcc, fcc, and hcp phases. The alloy Fe40Co40Mn10Cr10 exhibits outstanding cold workability arising from its deformation-driven transformation-induced plasticity, achieving a saturation magnetization of 2.0 T and a coercivity of 71 A/m after annealing at 400 degrees C. By reducing Mn and Cr, a single-phase bcc is stabilized in Fe42.5Co45.5Mn5.5Cr6.5, which increases resistivity to 85.4 mu Omega-cm while retaining magnetization at 1.9 T. These findings establish metastability control via processing and compositional tuning as an effective strategy to achieve concurrent enhancement of magnetization, resistivity, and formability for next-generation soft-magnetic alloys for high-efficiency and power-dense energy applications.

The energy efficiency of heat engines (gas and steam turbines) for electricity production and propulsion is determined by the Carnot cycle and scales with operating temperature. Commercial nickel- and cobalt-based superalloys melt near 1,500 °C and rapidly lose mechanical strength beyond 1,000 °C. Refractory metals melt well above 2,000 °C but have inherent manufacturability challenges that are barriers to adoption, such as high ductile-to-brittle transition temperatures. Using density functional theory-guided design, we demonstrate tailored local lattice distortions that promote phase-stable, non-equiatomic refractory concentrated solid solutions with both high ductility and strength. We exemplify this for single-phase, body-centred cubic Nb4Ta4V3Ti that exhibits castability, excellent room-temperature tensile yield strength (∼1 GPa) and ductility (approaching 20

Chemical short-range order (CSRO) critically shapes the properties of complex concentrated alloys (CCAs), yet its quantitative prediction remains limited by experimental ambiguity and the prohibitive cost of first-principles sampling across vast compositional spaces. Here we introduce a physics-informed machine-learning framework that predicts Warren-Cowley CSRO parameters for all binary pairs in multicomponent alloys directly from composition. Each alloy is encoded as a unique three-dimensional stack of binary interaction descriptors and mapped to a full matrix of first-nearest-neighbor CSRO parameters using an ensemble of variational autoencoders. Despite being trained on a limited dataset, the model accurately classifies ordering versus clustering, captures non-linear and non-monotonic compositional trends, and reveals that CSRO in CCAs emerges from the collective interplay of all atomic pairs rather than isolated interactions. Upon interrogating the model, we further observe that the CSRO of a given binary pair can be dominated by interactions among other pairs, underscoring the non-separable nature of chemical ordering in multicomponent alloys. By linking the predicted CSRO to changes in electronic structure and diffusion barriers, this work establishes a scalable, extendable route for integrating machine-learned short-range order into predictive alloy design.

Binary transition metal phosphides and their solid solutions have emerged as promising hydrogen evolution reaction (HER) catalysts. Although many research endeavors have adopted strategies to vary compositions to optimize catalytic performance, they mainly focus on binary structures, which represent only a small fraction of the abundant phase space of structure types among transition metal phosphides. The largely unexplored class of ternary and multinary ordered phosphides in catalysis comprises two or more metals with quite different chemical nature, concealing the structure-property relationships essential for advancing catalyst design. Here, we explored phosphides crystallizing in one of the most abundant ordered intermetallic structure types, -the ThCr2Si2 type, -where square nets of 3d transition metal M and P atoms are separated by layers of electropositive Ba cations. Four ternary BaM2P2 (M = Fe, Fe/Cu, Fe/Ni, Ni) catalysts were synthesized and characterized. BaNi2P2 showed high HER activity in acidic electrolyte, which required an overpotential, eta(10), of only 62 mV to drive current density j = -10 mA/cm(2) and high stability with a potential drop rate of 0.25 mV/h. BaNi2P2 outperformed other Ni-based catalysts, such as Ni2P and Ni5P4. Notably, at current densities above -170 mA/cm(2), BaNi2P2 outperformed the standard Pt electrode measured under identical conditions. Electronic structure analysis revealed a volcano-type activity trend among the four BaM2P2 catalysts based on their d-band center positions, highlighting the role of electropositive Ba cations in shifting the Ni-3d orbitals into an optimal position.

Aqueous zinc-ion batteries (AZIBs) have emerged as promising next-generation energy-storage systems on account of their inherent safety, low cost, and use of earth-abundant materials. Vanadium-sulfide cathode materials have recently attracted significant interest due to their fast Zn2+ diffusion and high reversible capacity, enabled by multivalent vanadium redox reactions and the good electronic conductivity of sulfur. In this work, we synthesized a VS2@S nanocomposite (VASN) cathode using the CVD technique to mitigate the structural instability and vanadium dissolution. The VASN delivers a high reversible capacity of 294 mAh g(-1) at 0.1 A g(-1) and excellent cycling stability, retaining similar to 95% of its capacity over 1600 cycles at 1 A g(-1). Furthermore, the Zn//VASN cell demonstrates an outstanding energy density of 470.56 Wh kg(-1) attributed to its unique nanostructure promoted ion accessibility. Further enhanced surface area, and reduced charge-transfer resistance across various temperatures results in improved Zn2+ diffusion kinetics dominated by intercalation-type pseudocapacitance. Additionally, the ex-situ analysis unveils the intercalation of Zn2+ ion accompanied by the reversible V4+ reduction. This study provides new insights into the design of sulfur-rich vanadium compounds and establishes an effective strategy for developing high-energy, durable aqueous Zn-ion batteries.

We have developed two Ni phosphide preparation methods allowing operando XAS surface-sensitive studies of well-defined bulk systems. For Ni K-edge XAS, a Ni2P phase-pure powder was sintered into a high-density pellet and polished for grazing incidence XAS. Ni sites were mildly affected by the acidic electrolyte prior to the HER, while the applied cathodic potential caused the reduction of Ni surface states beyond the states of as-prepared Ni2P. The computed fully H-covered Ni2P [0001] model describes the difference in the operando Ni K-edge GIXAS spectrum well. Upon turning the applied bias off, the Ni sites became immediately oxidized, forming NiO on the surface. Thus, the active phase during the HER is covalent Ni0 close to that in the intermetallic phosphides, and Ni2+ oxides formed after, and not during, the HER. For P K-edge XAS, Ni foam was phosphorized to form a thin Ni3P layer while preserving its high surface area. Upon immersion in the acidic electrolyte, the P sites underwent removal of P5+ phosphates and formed new P coordination, possibly due to the adsorption of protons from the electrolyte. These new P surface states were not affected by turning the cathodic current on and off as soon as the sample was immersed in the acidic electrolyte. However, the removal of the sample from the electrochemical cell and drying in air resulted in substantial depletion and oxidation of surface P. Echoing the observed Ni site chemistry during HER, XAS and XPS suggest that the in situ active P sites are different from the oxidized P states observed under ex situ conditions.

Chemical short-range order (CSRO) is prevalent across many metals and alloys and has recently gained particular attention in concentrated alloys. The advent of complex concentrated alloys has spurred renewed interest in understanding and controlling CSRO. Here, we review recent experimental and theoretical progress on CSRO, highlighting both advancements and ongoing controversies, particularly regarding its impact on the physical properties of concentrated alloys. For example, a highly debated issue is the effect of CSRO on mechanical strength, which remains unresolved due to limited experimental measurements confined to a narrow annealing-temperature range, even for widely studied alloys like CoCrNi Evaluation of the CSRO effects on various physical properties is critical to answer a central question: Can CSRO be transformed into a practical alloy-engineering tool? We also identify critical gaps in the experimental and theoretical frameworks to achieve this goal. Despite the extensive study of CSRO, there remains a need for methodologies that enable its practical application in alloy design. We explore potential solutions, emphasizing the promising roles of machine-learning potentials and additive manufacturing in creating novel avenues for CSRO control.

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. In this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1,000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa >50 at.%) NbTa-Ti-V and W-rich (> 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

An electronic‐structure‐centered perspective is presented on permanent‐magnet (PM) design, highlighting two key levers, that is, saturation magnetization ( M s ), governed by 3d ‐band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin‐orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT‐based MAE/ J ij extraction, atomistic‐spin and micromagnetic modeling, and high‐throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic‐structure accuracy for small MAE (sensitive to functional choice, Hubbard U , and many‐body effects), ii) finite‐temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC‐weighted and orbital‐resolved fingerprints). Deep dives into the electronic‐structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT‐ and quantum‐mechanics‐based descriptors for discovery. Unbiased, structure‐driven exploration, coupled with high‐throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply‐chain risks, on future needs of designing “critical‐element‐free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare‐earth‐lean or rare‐earth free, high‐performance, sustainable PMs.

Transition-metal phosphides (MPs) are promising earth-abundant catalysts for hydrogen evolution reactions (HERs) due to their remarkable activity and stability. To further improve their properties, facet control is a key strategy. The growth of shape-selected nanoparticles may substantially enhance electrocatalytic activity, but this approach requires fundamental studies of facet-specific catalytic properties. There are only a few reports on the facet effects of MPs, which leads to a limited understanding of the activity of each facet and hampers catalyst design. Here, we grew large hexagonal-prism-shaped single crystals of three representative M2P (M = Ni, Co, and Fe) catalysts using metal flux routes. Two facets of M2P single crystals were tested to study facet-dependent HER activities, and it was consistently demonstrated that for all M2P crystals, a tip facet [(0001) for Ni2P/Fe2P and (010) for Co2P] had a higher activity than the side facet [(1010) for Ni2P/Fe2P and (100) for Co2P]. HER activity between the same facet elucidated the activity ordered between different transition metals as Fe2P > Co2P > Ni2P under low-potential regions. At high applied potentials, this trend is reversed due to the differences in Tafel slopes, with Ni2P becoming the most active catalyst, such that the activity of the (0001) facet of Ni2P approaches that of Pt. The calculated surface density of states (DOS) of each facet and its local curvature were found to be a useful descriptor for the activity trends among different transition metals of the same facets.

We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and B content reduces MS from 1.81T (DFT 2.04 T) to 1.54 T (DFT 1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09T), Fe-5Si-5B (2.01T) and Fe-10Si-10B (1.54T) alloy compositions further support our findings. These trends are consistent with density functional theory (DFT) predictions, which link increased electronic disorder and band broadening to lower MS values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveals that MS is governed by a nonlinear interplay between Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudo-quaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe84.8Si0.5B9.4Cu0.8 P3.5C1), FINEMET (Fe73.5Si13.5B9 Cu1Nb3), NANOPERM (Fe88Zr7B4Cu1), and HITPERM (Fe44Co44Zr7B4Cu1. Our fundings demonstrate the potential of ML framework for accelerated search of high-performance, Co- and Ni-free, soft magnetic materials.

While recent theoretical studies have positioned noncollinear polar magnets with Cnv symmetry as compelling candidates for realizing topological magnetic phases and substantial intrinsic anomalous Hall conductivity, experimental realizations of the same in strongly correlated systems remain rare. Here, we present a large intrinsic anomalous Hall effect and extended topological magnetic ordering in Gd3Ni8Sn4 with hexagonal C6v symmetry. Observation of topological Hall response, corroborated by metamagnetic anomalies in isothermal magnetization, peak/hump features in field-evolution of ac susceptibility and longitudinal resistivity suggest the signature of topological magnetic phases. The anomalous Hall effect is quantitatively accounted for by the intrinsic Berry curvature-mediated mechanism. Our results underscore polar magnets as a promising platform to investigate a plethora of emergent electrodynamic responses rooted in the interplay between magnetism and topology.

Understanding how composition affects deformation mechanisms in austenitic stainless steels is essential for developing accurate predictive models of stress-induced failures and stress corrosion cracking. Nickel (Ni), an element classified as a critical element, plays a crucial role in these processes. It is important to examine how Ni concentration influences stacking fault energy (SFE) and, consequently, the deformation mechanisms of austenitic stainless steels. However, in commercial stainless steels, the effects of other alloying elements and impurities can obscure Ni's role, complicating efforts to isolate its impact. In this study, we use two high-purity Fe-Cr-Ni alloys to investigate how Ni concentration and SFE interact to alter deformation mechanisms and induce martensitic transformation. By combining in situ synchrotron X-ray diffraction (XRD) tensile testing and postmortem electron microscopy with density functional theory simulations, we gain precise insights into these phenomena. We find that the Fe18Cr10Ni (wt%) alloy, with its low SFE, exhibits higher stacking fault probability, deformation-induced martensitic transformation, and a lesser increase in dislocation density with plastic strain. In contrast, the Fe18Cr14Ni (wt%) alloy, with its higher SFE, shows enhanced deformation twinning and greater dislocation density with increasing strain. These findings from high-purity ternary alloys provide valuable insights that can guide the search for alternative elements to replace Ni while achieving similar effects on phase stability and deformation behavior.