Papers

Electrochemical impedance spectroscopy (EIS) is a valuable tool for non-invasive battery characterisation, providing a compact representation of physical processes over a wide range of time scales. Commonly, sinusoids at different frequencies are injected sequentially (single-sines). Alternatively, a multisine excitation (a sum of sines) is advantageous for reducing experiment time and allowing impedance to be measured during operation. In this work, we demonstrate high-fidelity multisine EIS measurements on Li-ion cells over a wide frequency range (20 mHz to 1 kHz), taken with a commercial potentiostat modified to give access to the underlying current and voltage time-series data, and compare these to single-sine EIS. We show how this allows the conditions of linearity and stationarity to be verified directly, which is not possible with impedance data alone. We then measure broadband impedance during charging and discharging, temperature change, and relaxation. Because impedance is a linearisation of the response about an operating point, each of these conditions probes a state that classical EIS excludes by definition: kinetics linearised about a non-zero current, thermally activated reaction rates, and relaxing concentration gradients, respectively. Operando multisine EIS is therefore not simply a faster measurement approach, but one that resolves behaviour that steady-state EIS cannot.

The iso-orbital indicator $\alpha = (\tau - \tau^\mathrm{vW})/\tau^\mathrm{UEG}$ is a key ingredient of meta-generalized gradient approximation (meta-GGA) functionals, but diverges in low-density tails , causing unphysical exchange potentials and systematic band gap errors as noted in [J. Chem. Phys. 150, 161101 (2019)]. We replace the denominator of $\alpha$ with a physically motivated Pauli KED drawn from the orbital-free DFT literature, eliminating the divergence in the low density atomic tail without any empirical regularization parameter. Testing two such enhancement factors: LKT and PGS, within the r$^2$SCAN and MS2 exchange functionals, we find that the modified indicators suppress spurious oscillations in the semilocal exchange potential and restore correct electron localization in atomic tails. For a ten-member cubic semiconductor benchmark, the band gap mean absolute error is reduced by 41.1 % for r$^2$SCAN@PGS and 48.8 % for MS2@PGS, while cohesive energy accuracy is largely preserved. The consistent improvement across two functionals with distinct constructions confirms a physical rather than functional specific origin, and motivates further development of meta-GGA functionals with constraint satisfying iso-orbital indicators.

Nonradiative charge transfer processes play a central role in a wide range of physical phenomena, including reliability phenomena in semiconductor devices such as bias temperature instability, hysteresis, random telegraph noise, and trap-assisted tunneling. nonradiative multiphonon (NMP) theory provides a physically rigorous framework for describing such charge transitions, but its full quantum-mechanical formulation is computationally too demanding for large-scale simulations. In this work, we present a systematic and implementation-oriented treatment of NMP-based models for practical large-scale simulations. Starting from the quantum-mechanical foundations of coupled electron–phonon dynamics, we derive computationally efficient approximations for charge capture and emission rates and clearly identify the underlying assumptions and validity regimes. In particular, we introduce an effective crossing-preserving approximation that yields fully analytic, numerically stable, and computationally inexpensive transition rates while retaining the essential quantum-mechanical physics. The resulting expressions are therefore well suited for large-scale device simulations, where capture coefficients must be evaluated repeatedly over broad multidimensional parameter spaces. Furthermore, we derive continuum formulations for transitions between localized defect states and extended electronic bands, enabling direct incorporation into semiconductor-device simulations. The resulting framework bridges microscopic defect physics and practical large-scale simulations of charge transfer processes in complex semiconductor devices. At the same time this work serves as a practical guide for implementing physically grounded NMP-based models, providing both a systematic derivation of the underlying theory and a clear guidance on the validity limits.

The composition-dependent magnetic properties of B2-ordered \zfr~alloys with substitutional disorder on the Fe sublattice are investigated using first-principles calculations within the coherent potential approximation. By systematically substituting Mn and Co on the Fe sublattice, we establish $d$-band filling as the primary control parameter governing magnetic stability in this itinerant system. Mn substitution (hole doping) shifts the Fermi level into the minority-spin bonding states, driving a collapse of spin polarization (crossing zero at $x \approx 0.5$) and the emergence of competing antiferromagnetic interactions ($\eta_\mathrm{Mn} < 0$). Even though the ferromagnetic configuration remains energetically well separated from the G-type AFM-II configuration across the studied range ($\Delta E$ up to $\sim$0.35~eV/atom), this exchange competition drives an “itinerant magnetic softness” that suppresses the Curie temperature by $\sim$450~K – a finite-temperature instability set by the near-cancellation of competing exchange interactions rather than by AFM–FM energy proximity. In contrast, Co substitution (electron doping) acts as a “magnetic hardener” by pinning the Fermi level within the majority-spin pseudogap, preserving high spin polarization ($|P| \approx 0.75$) and stabilizing ferromagnetic exchange across the full composition range. These results show that tuning the Fermi level relative to the pseudogap provides a systematic, microscopic framework for controlling magnetic stability in B2-ordered itinerant magnets, distinct from simple magneto-volume models.

The shape and arrangement of particles in a material determine its macroscopic properties. The generation of synthetic data with varying particle structure, often represented as 3D voxel images, combined with simulation of macroscopic properties reveals structure-property relations. Most particle generation models focus on single-particle characteristics like shape and size. We aim at fitting the topology of the particle system using tools from persistent homology. However, the large size of the required 3D image data makes existing methods computationally infeasible. We bridge this gap by introducing a novel graph representation of particle systems and transferring the computation of persistent homology from the image domain to the graph domain. This yields a postprocessing method for synthetic images of particle systems, that is independent of the underlying generation method and improves topological and geometrical agreement with real particle systems while preserving morphological characteristics such as the particle size distribution.

Commercial fusion energy requires materials that survive intense neutron bombardment whilst extracting extreme heat loads for conversion to electricity. The CuCrZr alloy, the leading heat-sink material for fusion reactors, derives its strength from a fine dispersion of nano-precipitates formed during prime-ageing heat-treatment. Whether this precipitation-hardening strategy can withstand fusion-relevant irradiation remains untested. Here we show, combining in situ transmission electron microscopy under heavy-ion irradiation and He implantation with thermodynamic and transmutation modelling, that the hardening precipitates dissolve under two opposing kinetic regimes: ballistic dissolution dominates at low temperatures, whilst dissolution and re-precipitation dominate at high temperatures. Although the accelerated dose rates inherent to ion irradiation shift the balance between ballistic mixing and thermal back-diffusion relative to reactor conditions, precipitate degradation at both kinetic extremes indicates that the prime-aged microstructure is unlikely to remain unaltered under prolonged neutron exposure. He bubbles and Kr-rich voids nucleate once vacancies become mobile, and transmutation over five service years irreversibly redirects the alloy chemistry towards Ni-Zr intermetallics. These three independent mechanisms converge to challenge the strategy on which CuCrZr performance depends, suggesting that the long-term performance of age-hardenable Cu-based heat-sink alloys in fusion reactors warrants further assessment. Our findings reveal a new materials challenge for fusion reactor design and commercialisation: the need for new Cu-based heat-sink alloys able to retain engineered strength whilst their chemistry is irreversibly rewritten - thermodynamically and ballistically - by the fusion neutron spectrum.

Interface-induced superconductivity in FeTe-based heterostructures provides a promising route toward topological superconductivity, yet the roles of the neighboring layers topology, symmetry, and electronic structure remain unresolved. In this work, we employ molecular beam epitaxy to grow Pb1-xSnxTe/FeTe bilayers and use angle-resolved photoemission spectroscopy to track the evolution of the Pb1-xSnxTe layer from a trivial insulator to a topological crystalline insulator hosting multiple Dirac surface states. Electrical transport measurements reveal robust superconductivity throughout the entire composition range, with a nearly constant superconducting transition temperature of ~12 K despite substantial changes in the electronic structure and topology of Pb1-xSnxTe. Double-coil mutual-inductance measurements further reveal comparable superfluid stiffness across the topological phase transition and nearly thickness-independent superfluid stiffness despite large variations in the constituent-layer thicknesses, demonstrating that superconductivity is confined near the interface. These results establish that superconductivity in FeTe-based heterostructures is largely insensitive to the topology, crystal symmetry, and detailed electronic structure of the neighboring layer, supporting a primary origin in modifications to the FeTe layer. The coexistence of interface-confined superconductivity and tunable multiple Dirac surface states in Pb1-xSnxTe/FeTe bilayers provides a versatile platform for exploring topological superconductivity and interactions among multiple Majorana zero modes.

The properties of strongly correlated electron materials exhibit a surprising sensitivity to small lattice distortions, providing an opportunity for their tuning by selective distortion driving, usually achieved by optical excitations. Using inelastic electron tunneling in scanning tunneling microscopy, we demonstrate that at the surface of a strongly correlated electron material, we can drive vibrational excitations out of equilibrium, by studying the dynamics of localized modes on the Pd-terminated surface of the delafossite oxide PdCrO2. This surface forms a tiling of hydrogen clusters of varying sizes and shapes upon hydrogen adsorption. Our findings reveal that vibrational excitations in the clusters exhibit longer lifetimes than on typical metal surfaces. Detailed analysis of the spectroscopy data reveals signatures of non-equilibrium effects in the excitations which we attribute to the extended lifetimes of these modes. Theoretical calculations support that the long-lived nature of the excitations is related to the unique properties of the substrate.

This study combines high-throughput density functional theory (DFT) calculations with machine learning (ML) to uncover the key descriptors governing the nitrogen reduction reaction in 47 intermetallic compounds (IMCs) composed of Co, Ni, Al, Zn, V, Fe, Cu, and Pt, and the adsorption energies of key intermediates (*N2, *N2H, and *NH3) were systematically evaluated across all accessible surface sites, yielding approximately 1,200 data points. Among the IMCs studied, Fe9Co7 and Fe3Co emerge as the most balanced catalysts, exhibiting favorable adsorption energies across all three intermediates. By incorporating intrinsic material properties along with local and global electronic descriptors including s-, p- and d-band centers and fillings, as well as Bader charges of atoms neighboring the adsorbate, predictive ML models were developed with mean absolute errors (MAEs) of 0.27 eV for *N2, 0.39 eV for *N2H, and 0.17 eV for *NH3 adsorption. Importantly, accurate adsorption-energy predictions were achieved using only 20 key features for *N2H and *NH3 and 38 features for *N2, enabling the use of simple and computationally efficient ML models. SHAP analysis indicates that p-band and s-band characteristics play a more prominent role in determining adsorption strength than the traditionally used d-band center, particularly for *N2. Beyond their established importance in systems containing p-block elements or nearly filled d-band metals, s- and p-orbitals are also found to contribute significantly to transition metal alloy activity such as Fe-Co. By challenging the d-band-centric paradigm and identifying s- and p-band descriptors as critical yet overlooked contributors, this work redefines the electronic descriptor space for intermetallic NRR catalysts and lays the groundwork for DFT-ML-guided discovery of non-noble, compositionally complex materials for sustainable ammonia synthesis.

First-principles calculations are rarely single solver calls: they are multistage workflows in which structures, settings, and saved densities must pass correctly between dependent stages. Language models can write a valid single input, but reliability collapses once stages are chained: a dropped dependency, wrong file handoff, or hallucinated keyword can invalidate results even when the solver runs, and such errors surface only after costly execution. We introduce LatticeMind, a validation-gated agentic framework that converts natural-language requests into executable, checkable density functional theory (DFT) projects. A workflow contract specifies required stages, files, and dependencies; deterministic validators inspect the project before any solver launch; failed checks trigger bounded repair; and reports remain traceable to project files. On a 44-case RESCU benchmark spanning total-energy, density-of-states, band-structure, magnetic, phonon workflows, three non-flagship LLM backends reproduced every reference value; 119 of 132 runs passed on the first validated plan and the rest after one repair cycle. Ablations identify the workflow contract and pre-execution validation as the principal reliability mechanisms; a conventional trial-and-repair baseline reaches 71% at twice the token cost per success. The same architecture reproduces every reference value on a 19-case VASP benchmark, demonstrating transfer across solvers. Beyond these task families, LatticeMind generated and validated the 23 inputs of a multistage study of Rh-induced electronic responses at a Rh$_6$/In$_x$Ga$_{1-x}$N photocatalyst interface. These results establish workflow contracts, deterministic validation, bounded repair, and traceable records as a practical foundation for reliable AI-assisted scientific computing, where preventable errors are caught before execution and the solver is never the first validator.

The disordered and defect-rich structure of amorphous solids forms heterogeneous, high-dimensional energy landscapes. Such an energy landscape can be described by a discrete-state network of transitions between stable energy minima. Under low-frequency mechanical oscillations, defect-mediated, thermally activated transitions provide a microscopic mechanism for mechanical dissipation that are the dominant cause of mechanical loss in the mirror coatings of ground based gravitational waves detectors. Using molecular simulations, we find spatially correlated and strongly interacting transitions that require a general network description instead of a superposition of independent two-level systems as traditionally assumed. An annealing study combined with an analysis of dominant relaxation paths in the energy landscape reveals novel mechanisms for reducing room temperature mechanical loss.

We classify the symmetry-constrained forms of the Berry curvature dipole $\mathbf{D}$, gyrotropic magnetic tensor $\mathbf{K}$, and magnetoelectric electro-optic (EO) tensor $\mathbf{G}$, which describe metallic optical and EO effects in time-reversal symmetric, noncentrosymmetric metals. We identify 11 space groups (SGs) in which $\mathbf{D}$ and $\mathbf{K}$ vanish by symmetry while $\mathbf{G}$ remains allowed, thereby providing a more direct route to observing the recently predicted magnetoelectric EO effects associated with $\mathbf{G}$. First-principles based calculations confirm that $\mathbf{D}$ and $\mathbf{K}$ vanish for representative materials, while $\mathbf{G}$ remains allowed and tunable via Fermi level shifting. We further show that the choices of SG and experimental configuration provide complementary paths for isolating $\mathbf{G}$-driven EO effects, including cases where $\mathbf{D}$ and $\mathbf{K}$ are also symmetry-allowed. In an oblique-incidence geometry, the $\mathbf{D}$-driven response produces a helicity-even absorption or gain correction, whereas the $\mathbf{G}$-driven response couples the $s$ and $p$ optical sectors and produces a bias-induced circular dichroism with a characteristic $\sin\theta\cos\theta$ angular dependence. This provides a direct experimental route for separating the $\mathbf{D}$- and $\mathbf{G}$-driven EO signatures in noncentrosymmetric metals.

The nonlinear Hall effect (NLHE) enables the generation of a transverse charge current in nonmagnetic materials with broken inversion symmetry while preserving time-reversal symmetry through the Berry curvature dipole (BCD). However, in crystals with $C_{3v}$ symmetry, the threefold rotational symmetry forces the BCD to vanish, thereby suppressing the intrinsic NLHE despite the presence of finite local Berry curvature. Here, using first-principles density functional theory combined with Wannier-based transport calculations, we demonstrate that uniaxial strain induces the NLHE in monolayer Janus AsTeBr by lowering the crystal symmetry from $C_{3v}$ to $C_{1}$ and generating a finite BCD. The resulting anisotropic redistribution of Berry-curvature hotspots produces pronounced nonlinear Hall conductivity and nonlinear Hall current, with the maximum response obtained at 2% tensile strain. To elucidate the accompanying electronic-structure evolution, we further investigate the strain-dependent optical properties through the joint density of states, dielectric function, optical absorption, and reflectance. The optical spectra exhibit a systematic red shift and enhanced low-energy interband transitions, consistent with the strain-induced reconstruction of the electronic structure. Our results establish a microscopic connection between symmetry breaking, Berry-phase geometry, nonlinear Hall transport, and optical response, demonstrating that uniaxial strain provides an effective strategy for tailoring multiple functional properties in Janus two-dimensional materials.

Quantitative prediction of rapid-solidification microstructures requires phase field models that represent the velocity dependence of interfacial properties, including solute partitioning, kinetic liquidus response, solute drag, and kinetic undercooling. These response functions control both microsegregation and morphology selection, but are difficult to prescribe accurately in phase field simulations that employ enlarged interfaces for numerical efficiency. We introduce an optimization-based calibration strategy that embeds target sharp-interface response functions into a dilute alloy phase field formulation by treating the interfacial diffusivity interpolation function as a response-matching degree of freedom. The optimized diffusivity functions are obtained from one-dimensional steady-state phase field solutions constrained to reproduce prescribed continuous-growth-model targets for velocity-dependent solute trapping and drag-modified liquidus kinetics. We demonstrate the accuracy and versatility of the calibrated model in dilute Al-Cu by reproducing the prescribed response functions for intermediate solute drag coefficients relevant to rapid solidification. Two-dimensional simulations are carried out over a range of drag coefficients and pulling velocities relevant to additive manufacturing, demonstrating that quantitative changes in the prescribed interface kinetics can systematically shift the morphology from cellular to banded growth. Additionally, we extend the formulation to dilute multicomponent alloys, enabling independent specification of equilibrium partition coefficients and liquidus slopes for multiple solute species. The framework separates the physical specification of nonequilibrium interface kinetics from their diffuse-interface realization and provides a practical route for embedding response functions into quantitative phase field computations.

This study examines phonon-mediated heat transfer across Si/SiC and SiC/diamond interfaces using non-equilibrium molecular dynamics simulations, emphasizing the influence of SiC polytypes and amorphous interlayers. For sharp interfaces, 4H-SiC exhibits considerably higher interfacial thermal conductance (ITC) than 3C-SiC, due to its broader active phonon spectrum and superior spectral matching with Si. While amorphous layers generally reduce ITC, a key observation is that an ultrathin 0.5-nm amorphous SiC (aSiC) layer can enhance heat transport in the Si/3C-SiC system: the ITC increases from 613 MW/m^2-K (sharp) to 716 MW/m^2-K, demonstrating a phonon-bridge effect. VDOS (vibrational density of states) analysis confirms that optimized ultrathin aSiC layers improve vibrational overlap and open additional phonon-transport channels. In contrast, thicker or silicon-rich amorphous layers significantly suppress ITC through enhanced inelastic phonon scattering. For SiC/diamond interfaces, any amorphous layer, particularly aSi, causes severe ITC degradation, highlighting the need for sharp, defect-free bonding to exploit diamond's high thermal conductivity.

Real-time time-dependent density functional theory provides a first-principles description of coherent electron dynamics in laser-driven solids, but its unitary formulation cannot capture the irreversible scattering, relaxation, and decoherence processes that drive excited carriers toward equilibrium. Here, we develop a dissipative rt-TDDFT framework in which first-principles electron-phonon interactions enter the evolution of the reduced one-body density matrix through self-energy-derived collision integrals within the Born-Markov approximation. The approach retains the quantum-coherent real-time propagation of the electronic system while introducing phonon-mediated transitions that redistribute carriers in energy and crystal momentum, thereby incorporating the microscopic momentum-transfer processes responsible for relaxation in real materials. The resulting framework provides a practical first-principles route to simulate relaxation, decoherence, and time-resolved spectroscopic signatures in realistic crystalline materials.

In the system of metallike hydride-electron donor mixtures, adsorbed hydrogen atoms are transformed into metallic states and alloyed. Dense itinerary s-electrons supplied by the electron donor cause a drop in the melting point of metallic hydrogen. As a result, this system reveals thermodynamical liquid activity. The coherent D2-D2 fusion and the D3-D3 chemonuclear fusion are enhanced with factors of 20 to 30 and 30 to 46 figures of magnitude respectively at T=460 K, hence the production of intense He ions of 23.8 MeV kinetic energy. The ions or alpha-particles induce enhanced cascade chemonuclear reactions towards diverse and useful element synthesis. This phenomenon is in the nature of the Big Bang nucleosynthesis that takes place in an inhomogeneous universe. An application of the alpha-induced chemonuclear reaction - i.e., the transmutation of 90Sr and 137Cs - is prescribed in this chapter. This system of metallike hydride-electron donor mixtures raises the possibility of radioactive waste vanishment. Stimulated by alpha-particle irradiation, trans-gold nuclei undergo exothermic alpha-cluster emissions, producing coherently line-up alpha-clusters. In the chemonuclear D-D fusion, the 4alpha-cluster reveals the thermodynamical activity of oxygen atoms. In the hydrogen-Ni nanopowder-Li mixtures, dispersively charged trans-gold atoms undergo enhanced chemonuclear alpha-cluster emissions, hence the mass synthesis of noble metals. The case of chemonuclear pion production is presented at the end of this chapter.

Electrical control of magnetism represents a fundamental route toward next-generation spintronic functionalities. In this Letter, we introduce a universal current-induced spin phenomenon in bilayer systems, termed the layer Edelstein effect (LEE), which serves as the natural counterpart of the layer Hall effect in real space. It is defined by the emergence of layer-resolved spin magnetizations with opposite components on the top and bottom layers, driven by an in-plane charge current and controllable by an external electric field. We establish the general existence of the LEE using a minimal bilayer $k \cdot p$ theory. By combining symmetry analysis with a general bilayer stacking framework, we derive a model-independent symmetry criterion demonstrating that the LEE is generically allowed in a broad class of nonmagnetic bilayer stacking systems. We further show that the LEE admits two universal manifestations: explicit layer-opposite spin magnetization components mandated directly by symmetry, and components become activated upon symmetry reduction by external electric fields. First-principles calculations on stacked bilayer MoSSe, MoTe$_2$ and WTe$_2$ confirm the predicted effect and illustrate their experimental feasibility. Our work establishes the LEE as a generic symmetry-governed response of bilayer systems, providing a unified conceptual framework for electrically generating and manipulating layer-resolved spin polarization.

The modification and enhancement of antiferromagnetic two-dimensional semiconductor is considered crucial for realizing novel electronic properties and facilitating promising applications. For this purpose, we investigate six antiferromagnetic 2D bilayer Janus YIBr structures with different stacking variations by means of first-principles calculation and an effective low-energy model. The calculation of magnetic anisotropy energy shows that the direction of easy axis varies with different stacking. First-principles-calculated energy bands reveal that there is a Dirac relativistic dispersion relation exists in the valence band in a wide energy window of 0.3 eV at least. The calculations for spin, atom properties and Berry curvature description show that there is spin, valley and layer coupling with spin splitting, valley polarization and valley Hall and layer Hall effects can be achieved in the bilayer Janus structures. Further analyses of the effect of external electric field can be used to control spin, valley and layer of the hole near the Fermi level and to realize an anomalous valley Hall effect. These can be useful in future exploration for novel properties, control methods and more functionalities in bilayer Janus structures.

Powder X-ray diffraction (XRD) is widely used for crystalline phase identification, and recent machine learning approaches have demonstrated remarkable capabilities in accelerating diffraction interpretation. However, reliable phase assignment still requires transparent, evidence-based validation, particularly for complex samples where interpretability and expert assessment remain essential. Search-match methods provide a robust and complementary strategy, yet many implementations are proprietary, limiting accessibility and reproducibility. Here, we introduce XMatcher, an open-source, evidence-driven framework that integrates diffraction databases, matching algorithms, and interactive visualization into a portable workflow. XMatcher generates theoretical diffraction libraries from crystal structures, retrieves candidate phases through chemical and diffraction constraints, applies global angular-shift correction and one-to-one peak matching, and reports quantitative agreement metrics together with peak-level evidence. Its AutoMix module extends identification to multiphase patterns by evaluating candidate phase combinations, estimating non-negative diffraction contributions, and visualizing phase-specific peak distributions. Through a local graphical interface, XMatcher enables ranked candidate inspection, interactive pattern comparison, PDF/CIF-based whole-pattern validation, and reproducible analysis export. By exposing both supporting and conflicting evidence rather than relying on a single similarity score, XMatcher provides an interpretable and reproducible platform for crystalline phase identification.

The dynamic fracture behaviour of two moving collinear Griffith cracks in an initially stressed dry sandy medium subjected to concentrated crack-face loading and moving punch pressure is investigated. A moving coordinate system transforms the transient problem into a steady state formulation, while the effects of initial stress and sandiness are incorporated into the governing equations. Fourier integral transforms are employed to obtain the characteristic equation and the transformed traction displacement relations. The crack face, symmetry, and outer surface conditions reduce the problem to coupled Cauchy type singular integral equations. For a sufficiently thick strip, an asymptotic kernel reduction is developed, and the resulting equations are solved analytically using the finite Hilbert transform. Closed form expressions are derived for the crack density functions, Mode I stress intensity factors at the inner and outer crack tips, and the crack opening displacement. Several limiting cases are recovered from the general formulation, providing analytical consistency checks. The results demonstrate the combined influence of crack speed, crack geometry, initial stress, sandiness, and moving punch loading on crack tip intensification and crack opening. The proposed analytical framework provides useful insights for fracture assessment and the design of transportation infrastructure, geotechnical systems, underground excavations, and other engineering structures involving dry sandy media subjected to moving loads.

Accurate and efficient modeling of magnetic potential energy surfaces remains challenging because spin-polarized first-principles calculations for diverse non-collinear spin-lattice configurations are computationally demanding. Here we introduce the Spin Tensor Equivariant Potential (STEP), a magnetic machine-learning interatomic potential that treats vector magnetic moments as continuous geometric degrees of freedom and embeds them in an equivariant representation. By coupling the central spin representation to its local spin-lattice environment through a Center-Environment Tensor Product, STEP introduces a physics-informed bias while preserving translational invariance and $\mathrm{SO}(3)$ equivariance and supporting feature-level time-reversal symmetrization. Learning-curve analysis on monolayer CrI$_3$ shows that STEP achieves pronounced data efficiency, with higher-order tensor channels and iterative center-environment couplings leading to steep learning curves for energy, force, and magnetic force errors. On public FeAl, CrN, and Fe benchmarks, STEP achieves competitive or improved accuracy compared with recent magnetic machine-learning potentials. Using a compact but representative CrI$_3$ dataset, STEP reproduces phonon dispersions and magnon spectra with high fidelity, capturing subtle anisotropic magnetic interactions. For Fe$_2$Mo$_3$O$_8$, STEP further provides a quantitative description of magnon–phonon hybridization and reproduces its characteristic magnon polaron dispersion. Finally, spin dynamics simulations driven by STEP yield Curie temperatures for monolayer CrI$_3$ and bcc Fe in good agreement with experiments. These results establish STEP as a physically informed, data-efficient, and scalable framework for modeling spin-lattice coupling, magnetic excitations, and finite-temperature magnetic behavior.

Isolating the quantum metric from the Berry curvature remains a central challenge in quantum materials, as the two geometric quantities nearly always coexist and their contributions are difficult to disentangle. We show that d-wave altermagnets, whose real Hamiltonian possesses strictly vanishing Berry curvature, constitute an ideal platform for overcoming this obstacle. Using the Magnus expansion and exact Floquet diagonalization, we demonstrate that linearly polarized off-resonant light drives an orbital-selective ferrovalley phase through a purely quantum-metric–mediated band-gap renormalization, with no Berry curvature contribution at any order. The orbital selectivity originates from the hopping anisotropy, which generates a pronounced metric anisotropy between the $d_{xz}$ and $d_{yz}$ orbitals, and the gap reduction is expressed analytically in terms of the quantum metric. The resulting valley gap difference provides a direct, quantitative measure of the quantum metric, accessible to spin-resolved ARPES and optical pump-probe spectroscopy. This establishes d-wave altermagnets as a pristine, tunable platform in which quantum metric effects can be isolated, controlled by light polarization, and read out through valley polarization.

Large language models (LLMs) can plan tool-mediated scientific work, but scientific instruments remain difficult to connect to such agents: vendor APIs may load only inside acquisition host processes, facilities may prohibit cloud-hosted agents, and natural-language interfaces can emit physically unreasonable arguments. We present a method for connecting local LLMs to scientific instruments through the Model Context Protocol (MCP). It combines: (1) a schema-bound tool surface that validates requests against physical bounds before adapter dispatch; (2) a vendor-neutral, host-process adapter pattern separating language-side reasoning from instrument-side execution; (3) a persistent lifecycle for long-running live-processing jobs; and (4) MCP-prompt-registered skills that compose typed tools into reusable multi-step protocols. Our open-source reference server exposes 30 typed tools, 5 live-job types, and 6 skills through a physics-plausible simulator implementing the same protocol surface. Validation is software-only: all 120 hardware-independent tests pass deterministically, while 15 local-LLM integration tests pass 12-15 of 15 across runs because of model nondeterminism. A preliminary single-run probe across five open tool-calling LLMs indicates that the schema-bound interface can be driven locally by small open-weight models without cloud dependency; it is not a benchmark and has no confidence intervals. The method provides a bounded, testable, and reproducible foundation for closed-loop agentic instrumentation research.

In electronic materials, flat bands are associated with compact electron localization, with implications for superconductivity, ferromagnetism and strongly correlated systems. The physical significance of their counterparts in elastic media is far less charted. Here we report a strategy to achieve elastic flat bands through an inertial retrofitting of classical lattice architectures. The idea is to alter the cell geometry to realize a network of inertial anchors, effectively partitioning the lattice into an array of weakly coupled emergent resonators, whose resonances appear as flat bands in the phonon spectrum. We demonstrate flat-band conditions that combine localized and extended state attributes and induce a giant response that is spatially and temporally persistent. Laser vibrometry experiments reveal three signatures of this mechanism: amplification up to two orders of magnitude compared to pass band and band gap conditions, multi-cell activation that is agnostic to the source location, and a persistent transient response even after several excitation cycles.

Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data. Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes

The Baldereschi point of a crystal is a wavevector in the Brillouin zone at which every smooth periodic function of wavevector lies close to its mean value. Although originally introduced in the context of one-electron methods, mean-value points are ideal for explicitly correlated many-electron methods such as quantum Monte Carlo simulations, which can only use a single Bloch wavevector in the Brillouin zone of a simulation supercell. We have therefore evaluated and tabulated the Baldereschi mean-value points of all fourteen three-dimensional Bravais lattices.

Unit quaternions are widely used in science to encode rotations because the quaternion product is more efficient than matrix product and more stable than Rodrigues product to calculate the composition of two rotations. However, quaternions in their usual form refers to a Cartesian basis; they cannot be used in crystallography as they are. The usual way to solve this issue to apply back-and-forth coordinate changes from the crystal basis to a Cartesian basis attached to the crystal with the help of the structure tensor. Here, we show that actually quaternions can be used directly in the crystal basis by generalizing the quaternion product law. In that aim, we introduced a matrix that we called cross tensor. It allows the calculation of the cross product in the crystal basis, a bit like the metric tensor allows it for the scalar product. We also show the cross tensor is proportional to the inverse of the metric tensor. The formula of crystallographic quaternion product is then given; it depends uniquely on the metric tensor. The application of the crystallographic quaternions to Electron Back Scatter Diffraction is discussed.

Optical microcavities are widely used to control the emission of organic semiconductors, but their ability to reshape the molecular pathways that precede emission remains largely unexplored. Here we show that embedding a ZnPc:TPBi blend in a planar Fabry-Pérot microcavity suppresses the formation of non-radiative exciplexes and removes bimolecular annihilation at high excitation densities, increasing the photoluminescence quantum yield by more than forty-fold under continuous-wave excitation. This enhancement is far larger than expected from the weak Purcell effect. Instead, transient spectroscopy, power-dependent photoluminescence and kinetic modelling point to a cavity-induced rebalancing of excited-state populations: long-range Förster energy transfer from ZnPc monomers to emissive aggregates is enhanced, allowing it to outcompete charge transfer to dark exciplexes. Electromagnetic calculations predict FRET enhancements of up to ~400-fold at relevant distances, consistent with the observed suppression of exciplex-mediated losses. Our results show that optical cavities can control not only how molecules emit, but also which excited states they form, opening a route to improved efficiency and reduced roll-off in organic optoelectronic and photonic devices.

Equivariant graph neural networks have proven effective tools for inference of material's properties directly from their structure. Traditionally, these have been applied such that they respect full $O(3)$ equivariance, so that any rotation or reflection of the input structure is respected in the model's output. While this works for general arrangements of atoms, additional symmetries of atomistic systems are left unleveraged. Furthermore, any symmetries of the filter functions are implicitly learned from the full dataset and not strictly enforced. In this work, we introduce point-group symmetry aware equivariant graph neural networks (PGEqNN) for materials science, with filter functions aligned with symmetry-aware indices for greater granularity in predictive tasks. With this architecture, we show that most of the predictive power of equivariant networks for tensorial elastic and dielectric datasets lies in the trivial subspaces of the point-group adapted bases. Exploiting this, an $A_1$-restricted variant matches or improves on its full point-group and $SO(3)$-partitioned counterparts while training fewer active parameters, yielding leaner models of equal accuracy.