AI for Science

Navigating the vast space of synthetically accessible molecules demands surrogate models that are interpretable and capable of handling multiple competing objectives at the same time. Deep learning approaches struggle to satisfy them under the computational constraints of quantum-level chemistry. Here, we introduce a modular pipeline that combines interpretable linear meta-learning models and adaptive-confidence uncertainty quantification into an Efficient Global Optimization (EGO) framework for multi-objective molecular discovery. For the first time, linear meta-learning is deployed in a multi-objective chemical search setting: by training across chemical objectives and cheap auxiliary properties, the meta-learned surrogates acquire transferable chemical knowledge that adapts rapidly to new objectives from limited data. Evaluated empirically on a real large scale search for spin-crossover metal-organic complexes, the baseline performs 78% worse in Pareto sense than the meta-learning alternative. We also address the calibration challenges inherent to active search. Since optimal candidates typically lie precisely in the distributional tails, standard uncertainty estimates fail. We introduce an adaptive confidence-tuning algorithm that dynamically recalibrates the exploration-exploitation trade-off as the molecular search evolves. Empirically, dynamic confidence tuning further dominates over 50% of the statically calibrated front.

Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge. In this study, we propose a hybrid GNN--FEM framework for efficient and generalizable phase-field fracture modeling. While phase-field approaches provide a robust variational framework for simulating complex crack evolution, their high computational cost limits practical applications because they require solving coupled, nonlinear, and history-dependent systems within an incremental finite element procedure. To address this challenge, a graph neural network surrogate is integrated into the conventional staggered scheme, replacing the phase-field update at each load increment while retaining the FEM-based displacement solver to enforce mechanical equilibrium and boundary conditions. By preserving the incremental solution structure, the framework remains consistent with history-dependent fracture evolution without requiring the surrogate to approximate the full solution trajectory. This selective surrogate strategy emphasizes the identification of a physically meaningful and incrementally structured learning target, rather than relying on brute-force data generation to learn the full fracture process. The proposed framework achieves strong generalization across varying geometries, loading conditions, material properties, and discretizations through dimensionless feature design, a graph-based formulation on mesh-based domains, and a physics-informed loss derived from the governing phase-field equation. Numerical experiments demonstrate that the hybrid approach reduces computational cost while maintaining accuracy compared with conventional FEM, and exhibits robust predictive performance across diverse problem settings.

Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial. This study proposes a physics-informed framework for discovering yield functions from full-field displacement data and reaction force data, without stress observations, plastic strain measurements, direct yield surface data, or a prescribed parametric yield function. The framework identifies the yield function as a mechanically constrained constitutive component inside elastoplastic stress integration, rather than through direct stress-space supervision. The yield function is represented by a convex neural network that enforces convexity and positive homogeneity of degree one while imposing the assumed tension-compression symmetry, and this neural yield function is trained with a differentiable stress update and a physics-informed force equilibrium loss across multiple loading cases. The proposed framework is validated using finite element (FE) benchmark studies with von Mises, Hill 1948, and Yld2000-2d yield functions, assessing yield contour agreement, displacement-noise sensitivity, identifiability through plastically active stress states, epistemic uncertainty, and polynomial-surrogate deployment. This study provides a mechanics-constrained pathway for discovering anisotropic yield functions from displacement and force data while keeping the identified component within the structure of elastoplastic stress integration.

Mineral surfaces govern interfacial reactivity in carbon mineralization, geo-energy storage, contaminant immobilization, heterogeneous catalysis and electrochemical interface engineering. Yet atomistic simulations often rely on commonly used facets or facet-level stability criteria, while distinct atomic terminations of the same crystallographic orientation are rarely resolved systematically because experimental characterization and density functional theory (DFT) calculations remain costly across large surface spaces. Here we present MinSurf, a high-throughput framework that resolves mineral surface selection as a surface-energy and morphology problem. MinSurf integrates surface enumeration, DFT labelling, machine-learning interatomic potentials and Wulff construction to predict stable terminations, surface-energy landscapes and equilibrium crystal morphologies. Applied to ten representative minerals, MinSurfSet comprises 764 surface slabs, with 90 corresponding oriented unit cells constructed as bulk references for surface-energy evaluation. The resulting MinNEP model predicts DFT surface energies with a mean absolute error of 0.0119 eV per Angstrom squared and achieves an overall acceleration of 1.14 x 10^4 relative to DFT. MinNEP preserves the DFT-derived morphology-determining surface-energy hierarchy and reproduces the dominant Wulff-exposed facets, while X-ray diffraction provides an independent crystallographic consistency check for alpha-quartz benchmark. By linking atomic terminations, surface energies and equilibrium morphologies, MinSurf provides reproducible and physically representative surface models for high-throughput simulations of mineral interfaces across energy, environmental and advanced inorganic materials.

High-entropy alloys (HEAs) have attracted sustained interest since their introduction by Cantor et al. and Yeh et al. because multi-principal-element compositions can exhibit unusual combinations of strength, thermal stability, and functional performance. A recurring problem in HEA design is determining whether a candidate composition is likely to form a single-phase solid solution or instead separate into multiple phases or intermetallic compounds. That question sits early in the alloy-design workflow because it shapes which compositions require further thermodynamic analysis, synthesis, and experimental validation. HEACalculator is an open-source Python package for calculating thermodynamic and structural descriptors used in HEA research and for evaluating published solid-solution formation rules in a single place. It computes sixteen commonly used quantities, including mixing enthalpy, configurational entropy, valence electron concentration, Hume-Rothery electron-to-atom ratio, atomic size mismatch, electronegativity mismatch, and derived stability parameters such as Omega, Lambda, and Phi, and it evaluates eight published prediction criteria. The package combines a curated elemental and binary-interaction dataset with three access modes: a command-line interface (CLI), a desktop graphical user interface (GUI), and a Python application programming interface (API) for programmatic use in notebooks and screening workflows.

Defect-driven phase instability critically influences the structural reliability of ultrawide bandgap oxides, yet direct nanoscale metrics linking local chemistry to structural transformation remain limited. Here, we introduce a coordination-sensitive atom probe tomography framework that quantitatively resolves reductions in local cation coordination and links them directly to defect-driven phase transformation. Using Si-doped beta-(AlxGa1-x)2O3 heterostructures with controlled Al composition (6-17%) and doping levels (10^17-10^20 cm^-3), we show that gamma-phase inclusions emerge exclusively under the combined conditions of elevated Al content and heavy Si doping. Two-dimensional compositional mapping reveals pronounced lateral Al/Ga inhomogeneity in these regions, while nearest-neighbor and radial distribution analyses quantitatively resolve a significant reduction in first-shell Ga coordination, consistent with local cation deficiency. Correlative scanning transmission electron microscopy confirms that these coordination-depleted regions coincide spatially with gamma-phase inclusions. Density functional theory further supports this mechanism, demonstrating that Al incorporation reduces monoclinic lattice stability and, in conjunction with donor-induced vacancy formation, facilitates vacancy-mediated cation rearrangement and coordination collapse. Together, these results establish coordination loss as a measurable nanoscale signature directly linked to defect-driven phase instability. This framework provides a generalizable approach for probing defect-driven phase instability in doped and alloyed ultrawide bandgap semiconductors.

Using density functional theory, we have performed a systematic study of the Heisenberg pairwise exchange interaction and the beyond Heisenberg multi-spin higher-order exchange interactions in unsupported transition-metal trilayers in the presence of external electric fields. The systems consist of a hexagonal atomic Fe layer sandwiched between 4$d$ (Ru, Rh, and Pd) and 5$d$ (Ir) transition-metal layers. Both fcc and hcp stackings of the 4$d$ overlayer have been taken into account. To scan a large part of the magnetic phase space, we have calculated the energy dispersion of spin spirals without and with applied electric fields up to $\pm 1.0$ V/Å. We find that the energy dispersion remains qualitatively the same upon applying the electric fields and the magnetic ground state remains unchanged. The exchange constants obtained by fitting the energy dispersions exhibit a linear dependence on the electric field up to values of about $\pm 0.5$ V/Å. The sign of the calculated pairwise and higher-order exchange constants remain unchanged with electric field, however, their values and field induced variation are sensitive to the 4$d$ overlayer. The changes are on the order of a few percent for the nearest-neighbor exchange constant and up to a few ten percent for beyond nearest-neighbor constants. The higher-order exchange constants are calculated based on the total energies of multi-$Q$ states, such as the $uudd$ and the 3$Q$ state. Similar to the pairwise exchange constants, we find a nearly linear field dependence of the higher order constants at small electric fields and variations of up to ten percent. We study the spin-dependent screening of the electric field for the three trilayers based on the spin- and orbital-decomposed electronic states.

We present a data-efficient framework for constructing general classical spin Hamiltonians by combining the spin-cluster expansion (SCE) with fully self-consistent noncollinear spin density functional theory (DFT). The key idea is to fit the SCE model to magnetic torques rather than to total energies. Because torques are site-resolved vectors, each spin configuration provides many informative regression targets, improving conditioning and substantially reducing the number of required DFT calculations, especially for large supercells. Applied to the B20-type chiral magnets ${\rm Mn}_{1-x}{\rm Fe}_{x}{\rm Ge}$ and ${\rm Fe}_{1-y}{\rm Co}_{y}{\rm Ge}$, the resulting SCE models determine full pairwise exchange tensors -- including isotropic exchange, symmetric anisotropic exchange, and the Dzyaloshinskii--Moriya interaction -- and predict the helical spin period via a micromagnetic mapping. The composition trends and the divergence of the period at the chirality sign-change point are well reproduced, in agreement with experiment. Moreover, the systematic nature of SCE enables controlled assessment of interaction order: as the training spin configurations become more disordered, the lowest-order model loses torque accuracy, whereas including higher-order interactions restores predictive power. These advances enable near-DFT-accurate spin models for finite-temperature magnetism and complex spin textures at modest computational cost, providing an extensible route to quantitative first-principles parameterization and predictive materials design. An open-source implementation is available as a Julia package, \textit{Magesty.jl}.

Creating superior lanthanide-activated inorganic phosphors is pivotal for advancing energy-efficient LED lighting and backlit flat panel displays. The most fundamental property these luminescent materials must possess is effective absorption/excitation by a blue InGaN LED for practical conversion into white light. The 5$d_1$ excited state energy level of lanthanides, which determines the excitation peak position, is influenced by the inorganic host structure, including the local environment, crystal structure, and composition, making it challenging to predict in advance. This study introduces a new extreme gradient boosting machine learning method that quantitatively determines a phosphor's longest (lowest energy) excitation wavelength. We focus on the Ce$^{3+}$ 4$f$ $\rightarrow$ 5$d$ transition due to its well-defined 5$d_1$ energy level observed in excitation and diffuse reflectance spectra. The model was trained on experimental data for 357 Ce$^{3+}$ cation substitution sites sourced from literature and in-house measurements and ultimately experimentally validated through the successful synthesis of a novel, blue-excited, green-emitting phosphor: Ca$_2$SrSc$_6$O$_{12}$:Ce$^{3+}$. This compound's excitation under commercial blue LED wavelength aligned remarkably well with the model's predictions. These results highlight the transformative potential of data-driven approaches in expediting the discovery of blue-absorbing phosphors for next-generation LED lighting.

Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose configurational landscape is poorly charted, as subtle energy differences govern the competing minima and pathways. Here we build an interstitial-focused dataset by active learning and benchmark three machine-learning interatomic potentials -- GAP, NEP and the equivariant MACE -- against density functional theory for energies, forces and migration barriers. MACE reproduces the reference energetics and relative stabilities, whereas the others can misorder the ground states. Annealing molecular dynamics with the validated potentials uncovers a series of previously unreported carbon interstitial clusters, from di- to octa-interstitials -- several introducing in-gap states of interest as colour centres -- and shows that their metastability is governed by kinetically accessible pathways rather than energetic ordering. These results chart the interstitial defect landscape and accelerate defect discovery for quantum technologies.

Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training strategy remains computationally demanding, limiting systematic exploration of model architectures and training protocols. Here, we present TorchNEP, a PyTorch-based implementation of NEP that combines analytically derived gradients, adaptive optimization, and a two-stage training strategy. TorchNEP accelerates training by more than two orders of magnitude while maintaining full compatibility with existing NEP models. We further show that the improvement in predictive accuracy primarily originates from the two-stage training protocol rather than the optimization algorithm itself. Across diverse benchmark datasets, TorchNEP consistently improves force and stress predictions while maintaining comparable or improved energy accuracy. Benchmark evaluations on elemental and alloy systems demonstrate enhanced predictive performance for both atomic configurations and key materials properties. Furthermore, we show that increasing model complexity does not necessarily improve predictive performance despite reducing training errors. Overall, TorchNEP provides an efficient and flexible training framework for developing more accurate and robust machine-learned interatomic potentials.

Density-potential inversion for periodic systems within Moreau-Yosida-regularised density-functional theory is investigated, both theoretically and numerically. We develop the framework in a periodic homogeneous Sobolev space and use it to recover the exchange-correlation potential of Kohn-Sham theory through a limiting procedure. A key analytical ingredient is the proof of lower semicontinuity of the non-interacting kinetic-energy functional in the chosen topology. The proximal mapping, together with its algorithmic evaluation, plays a central role in the resulting inversion scheme. Numerical experiments illustrate the performance and properties of the method for both the Kohn-Sham and Gross-Pitaevskii equations.

The tunability and control of topological edge/surface states are crucial for the development of new device applications. In this work, by combining first-principles calculations and Wannier-based tight-binding methods, we show the emergence of quantum spin Hall phases in van der Waals heterostructures formed by stacking two trivial quintuple layers from the Bi$_2$Te$_3$ family. We demonstrate the tunability of the edge states under interlayer strain and external electric field effects, suggesting the possibility of switching topological edge states on/off by external control. Additionally, the quantum spin Hall edge channels remain robust against interlayer twist, highlighting their stability against external perturbations. Our results provide a new way to create and manipulate two-dimensional topological phases in systems based on Bi$_2$Te$_3$ family, which can be valuable for practical applications, such as topological field effect transistors and spintronic devices.

We investigate the magnetic ground states and transverse spin excitations of bilayer and trilayer nickelates within a multi-orbital itinerant framework. For the bilayer system, although Hartree-Fock calculations slightly favor a double-stripe order, the calculated excitation spectrum of the single-stripe state, characterized by an anisotropic low-energy cone at $Q_{\text{BL}}$ and isotropic high-energy excitations near $Γ$, exhibits good qualitative agreement with recent RIXS and neutron scattering experiments. We further identify mirror-even optical interlayer modes at $Q_{\text{BL}}$ whose energies match the mirror-odd modes at $Γ$. For the trilayer system, both mirror-odd and mirror-even spin-density-wave states can be stabilized near $Q_{\text{TL}}$, with the mirror-odd state lower in energy in the parameter regime studied. The mirror-odd state hosts an additional nearly gapless mode dominated by the middle layer, while the mirror-even state contains only one acoustic branch together with two gapped optical modes. Comparison with available RIXS data favors the mirror-odd spin-density-wave scenario. Our results show that magnetic excitations provide a sensitive probe of the magnetic order and support a common itinerant origin of magnetism in multilayer nickelates.

Amorphous silicon (a-Si) is understood to be the canonical continuous random network material, ideally defined by fully fourfold coordination. Here, we show that a defect-free ('ideal') model of a-Si from machine-learning-driven molecular-dynamics simulations [L. A. M. Rosset et al., Nat. Commun. 16, 2360 (2025)], subsequently evaluated with hybrid-level density-functional theory computations, can accurately reproduce the experimentally observed electronic bandgap. We compare this model with one resulting from the Wooten-Winer-Weaire (WWW) bond-switching approach and with other recent approximants to ideal a-Si. More broadly, our work provides a platform for studies of band tails, optical properties, and transport in a-Si.

The topological heavy-fermion model provides a minimal framework for describing the coexistence of localized moments and itinerant Dirac electrons in magic-angle twisted bilayer graphene (MATBG). Several analytical and numerical methods have been applied to this model; however, whether they provide a realistic description of MATBG remains incompletely understood. In this work, we develop an Hubbard operator approach that incorporates non-local correlations beyond the single-site limit. We benchmark the approximate calculations against numerically exact determinant quantum Monte Carlo simulations of a lattice-regularized model. We show that commonly used local approximations, such as Hubbard-I, fail to capture the coupling between localized and itinerant degrees of freedom, leading to incorrect spectral properties in the local-moment regime. In contrast, the Hubbard operator method provides a controlled description of both correlation functions and spectral features over a regime of parameters, in good agreement with exact numerical methods.

We use density functional theory and model Hamiltonians to reveal large spin splitting of bands localized at low-symmetry, ferromagnetic surfaces of bulk antiferromagnets (AFMs). There is great interest in finding new material platforms combining the robustness and ultrafast dynamics of AFMs with large, functional spin splitting which is often restricted to ferromagnets. Here, we show that a subset of AFM surfaces which have symmetry-allowed magnetization can host large spin splitting via bulk degeneracy lifting of sublattice-resolved exchange splittings. Using model Hamiltonians, we show that the spin splitting is maximized for two ferromagnetic surface motifs: terminations with single uncompensated magnetic sublattices, and two-sublattice surfaces whose sublattices are magnetically and electronically compensated in the bulk, but acquire distinct crystal field environments via surface truncation. The latter case can yield FM-like spin splitting magnitudes while also having vanishingly small uncompensated magnetization. In contrast, when surface magnetization arises from relativistic canting on symmetry-connected sublattices, the spin splitting is expected to be small. We confirm these predictions with first-principles calculations of $\mathrm{Cr_2O_3}$ and $\mathrm{FeF_2}$, finding splittings from $\sim10\mathrm{meV}$-$\sim1\mathrm{eV}$ depending on the surface in question. Our findings point to intrinsic surface symmetry breaking as a route to large, functional spin splitting in an expanded range of AFM materials.

Accurate modeling of dielectric screening in van der Waals (vdW) heterostructures is essential for predicting photonic and optoelectronic properties - yet conventional first-principles methods are often hindered by incommensurate lattices and prohibitive computational costs. In this work, we introduce the microscopic Quantum Electrostatic Heterostructure (mQEH) method. mQEH employs a hierarchical and systematically improvable basis set to describe potentials and induced densities, eliminating the need for arbitrary geometric cutoffs and ensuring accurate screening descriptions at all length scales. The mQEH method is combined with a layer projected Bethe-Salpeter Equation (BSE) to enable calculations of optical spectra of experimentally relevant lattice-mismatched vdW heterostructures. Applying the mQEH-BSE framework to a series of transition-metal dichalcogenide (TMD) heterobilayers, we obtain absorption spectra and momentum-indirect exciton energies in excellent agreement with experiment. The framework provides a computationally efficient route to predictive modeling and design of vdW heterostructures with tailored optical properties.

Sub-bandgap optical absorption in AlN between 2 eV and 4 eV is widely observed, but its microscopic origin remains contested. Using photo-induced electron paramagnetic resonance (photo-EPR) and optical absorption spectroscopy on the same samples, we demonstrate a correlation between this absorption band and the neutral charge state of substitutional carbon on the nitrogen site (C$_N$). Hybrid functional calculations of the optical absorption spectra show that a transition involving C$_N$ and the valence band occurs near 3.3 eV, which agrees well with a peak identified within the measured optical absorption between 2 eV and 4 eV. This conclusion requires the combined ability to manipulate the charge state of carbon using photo-EPR and to use first-principles calculations of the absorption line shape that account for the dispersion of the valence band and the energy dependence of the optical matrix elements.

The growth of a thin film of a high-entropy AlCoCuFeNi alloy on a silicon (100) substrate was studied using molecular dynamics modeling. The simulation was carried out using the embedded atom model to describe the interactions among Al, Co, Cu, Ni, and Fe atoms. The interaction between Al, Co, Cu, Fe, Ni atoms and the Si substrate was modeled using the Lennard-Jones potential, while the interaction between silicon atoms was described using the Stillinger-Weber potential. The total simulation time was 50 ns. It was found that small clusters were formed at the first stage of deposition and that crystallization started after approximately 5 ns of simulation, when the characteristic cluster size was about 2 nm. At the end of the simulation, after 50 ns of modeling, the simulated film contained face-centered cubic, body-centered cubic, hexagonal close-packed, and amorphous phases. Analysis of the radial distribution function made it possible to determine nearest-neighbor distances and estimate the lattice parameters of these phases.

Identification of the particle interaction potential is a challenging and important task in dusty plasma, colloids, and smart materials as it allows the characterization of structure formation and helps predict phase transitions. With the advent of machine learning methods, this interaction can be extracted from particle position data, leading to a generalizable expression which is applicable in different systems. Methods such as sparse regression aim to provide a physically interpretable model that can generalize well, while avoiding unnecessary complexity due to overfitting. In this work, we present the use of the Sparse Identification of Nonlinear Dynamics (SINDy) with the weak formulation to learn equations of motion for noisy data from simple simulations of two dust particles interacting with a Yukawa (shielded Coulomb) potential. The application of these methods to experimental dusty plasma data is discussed, particularly in the case of simulation data and glass box experiments in RF discharge gravity environments and DC discharge microgravity environments, such as the Plasmakristall-4 (PK-4) experiment.

While correlated phenomena of flat bands have been extensively studied in twisted systems, the ordered states that emerge from interactions in the intrinsic flat bands of kagome lattice materials remain largely unexplored. The newly discovered kagome metal CsCr3Sb5 offers a unique and rich platform for this research, as its multi-orbital flat bands at the Fermi surface result in a complex interplay of pressurized superconductivity, antiferromagnetism, a structural phase transition, and density wave orders. Here, using ultrafast optical techniques, we provide strong spectroscopic evidence for a charge density wave transition in CsCr3Sb5, resolving previous ambiguities. Crucially, we identify rotational symmetry breaking that manifests as a three-state Potts-type nematicity. Our elastoresistance measurements directly demonstrate the electronic origin of this order, as the rotational-symmetry-breaking E2g component of the elastoresistance shows a divergent behaviour around the transition temperature. This exotic nematicity results from the lifting of degeneracy of the multi-orbital flat bands, akin to phenomena seen in certain iron-based superconductors. Our study pioneers the investigation of ultrafast dynamics in flat-band systems at the Fermi surface, offering new insights into the interactions between multiple elementary excitations in strongly correlated systems.

Magnesium ions play an essential role in many biological processes but remain challenging to model in biomolecular simulations. Despite considerable scientific effort, classical force fields fail to simultaneously reproduce key structural, thermodynamic and kinetic solution properties, likely due to their inability to explicitly account for quantum many-body effects. Here, we develop and systematically benchmark MACE neural network potentials (NNPs) for aqueous MgCl$_2$ solutions trained on revPBE-D3/zd and revPBE0-D3/zd density functional theory reference data and assess their ability to reproduce a broad range of experimental solution properties including the structure of the first hydration shell, diffusion coefficient, activity derivative, water-exchange rate and mechanism as well as solvation free energy. Both NNPs accurately reproduce the octahedral structure of the first hydration shell, ion pairing properties and diffusion coefficients. Combining the NNPs with transition path sampling and other enhanced sampling techniques allows us to capture the rare event of water exchange in the first hydration shell of Mg$^{2+}$ revealing a dissociative exchange mechanism. Transition interface sampling yields exchange rates within one order of magnitude of experiment, representing a substantial improvement over classical dissociative force fields. In contrast, the NNP-derived solvation free energy significantly underestimates the experimental value, revealing a limitation of the present local NNP architectures for describing ion solvation thermodynamics. Our results demonstrate that DFT-trained NNPs can accurately describe Mg$^{2+}$ hydration structure, diffusion, ion pairing, and exchange kinetics, while highlighting the need for explicit long-range electrostatic treatments to achieve quantitative agreement with experimental ion solvation free energies.

Many industrial applications and biological scenarios involve the interdiffusion of two polymeric species. Motivated by biological subcellular source-driven processes, we study polymer-polymer interdiffusion problems in the absence or the presence of a polymeric source, for both unentangled and entangled scenarios. Utilizing a two-fluid formalism, we arrive at scaling relations, self-similar reductions, and analytical solutions, which are confirmed with one- and two-dimensional numerical simulations. The introduction of a source term breaks the self-similar structure, modifying the boundary conditions and the domain of integration. Nevertheless, we show that the front characteristics of the diffusing droplet exhibit similar spatial structures as in the absence of a source. Our results allow deeper understanding of polymer-polymer interdiffusion and nonlinear transport, especially in the presence of a source.

Electric field control of magnetic order in two-dimensional (2D) van der Waals magnets is a central goal for low-power spin-based technologies. In the ambient-stable antiferromagnet CrSBr, strong magnetic anisotropy and robust exciton-spin coupling provide a favorable platform, yet deterministic electric field control of its magnetic phases has not been achieved. Here we demonstrate electric-field-driven reversible switching between antiferromagnetic and ferromagnetic states in dual-gated bilayer CrSBr without intentional carrier doping. In parallel, photoluminescence measurements resolve a tightly bound interlayer exciton with an intrinsic dipole moment of only ~1 e angstrom. The electric field dependence of the magnetic phase transition reveals two coexisting mechanisms: a linear magnetoelectric effect in the antiferromagnetic state and an electric-field-modulated interlayer exchange coupling. Their interplay accounts for the asymmetric evolution of the critical magnetic field. Our results establish bilayer CrSBr as a promising 2D material for electrically controlled spin-optoelectronic functionalities.

This study establishes a route to the Hartree--Fock (HF) limit for molecules and solids within the linearized augmented plane wave (LAPW) framework. We remove current limitations of the standard LAPW approach to nonlocal exchange by constructing radial basis functions and core orbitals consistently with the HF Hamiltonian. The presented method yields total energies of molecules and solids with a precision of a few $μ$Ha, and we use it to provide reference data for 14 semiconductors and insulators. For the systems considered in this study, the standard approach based on (semi)local potentials for constructing radial basis functions and core orbitals remains highly precise for practical relative energies, including molecular and solid-state formation energies and Si self-interstitial defect formation energies. More broadly, the results provide stringent all-electron benchmarks for basis-set and pseudopotential assessment, improve error control in hybrid-functional calculations within LAPW, and open the way to X-ray spectroscopy simulations within LAPW based directly on hybrid-functional core orbitals.

The emergence of non-relativistic spin splitting (NRSS) in altermagnetic systems has introduced a new paradigm in antiferromagnets with vanishing net magnetization. Although sliding-induced valley-polarized phases have recently been demonstrated in two-dimensional altermagnets, the observed valley-polarized state represents only a partial manifestation of altermagnetism, and a comprehensive classification based on spinsplitting characteristics remains lacking. Here, using first-principles calculations, general stacking theory, and spin-Laue symmetry analysis, we propose a coupled quasialtermagnetic state representing a distinct subclass of altermagnetism, in which reversible type-IV NRSS is controlled through interlayer sliding. Accordingly, the sliding-induced phases are classified into two categories: altermagnetic and quasi-altermagnetic states. We establish a direct correspondence between reciprocal-space spin splitting and real-space switching between the two quasi-altermagnetic states. Importantly, the spin-polarized bands in these states remain spin split at Γ point even in the absence of spin-orbit coupling (SOC), distinguishing them within the proposed classification framework. To demonstrate the interplay between altermagnetic and quasi-altermagnetic states, we investigate the two-dimensional Lieb-lattice material Mn2WS4 and its Janus derivative Mn2WS2Se2, analysing how changes in the local environment influence the different magnetic phases. Importantly, the underlying mechanism is broadly applicable to a wide class of twodimensional square-lattice systems. We further investigate the effects of SOC, focusing on spin texture and transport signatures in coupled quasi-altermagnetic states.

Valley polarization in 2D TMDs is promising for low-power valleytronic and spin-valley information processing, but time-reversal symmetry in pristine nonmagnetic TMDs keeps the K+ and K- valleys degenerate, limiting device applications. In this work, we investigated the structural stability, electronic properties, and tunable valley polarization of V-alloyed MoTe2 monolayer, Mo0.75V0.25Te2, using first-principles density functional theory (DFT) calculations. Substitutional alloying of MoTe2 with V introduced magnetic exchange interaction, which, together with spin-orbit coupling (SOC), lifted the valley degeneracy at the unequal valleys. The alloyed structure was found to be energetically and dynamically stable due to the absence of imaginary phonon modes. In pristine MoTe2, SOC produced spin splittings of 34.0 meV and 218.9 meV in the conduction bands and valence bands, respectively, but no valley polarization was observed. In contrast, Mo0.75V0.25Te2 exhibited spontaneous valley polarization of 37.3 meV in the conduction band and 78.2 meV in the valence band. The valley polarization was further enhanced by external electric fields and biaxial strain. A transverse electric field along the crystal c axis produced the maximum valley splitting of 132.8 meV in the valence band, whereas biaxial tensile strain increased the valence band valley splitting up to 160.8 meV. The maximum conduction band valley splitting reached 54.4 meV under 2% biaxial compressive strain. These results demonstrated that V alloying, combined with electric-field and strain engineering, provides an effective strategy for achieving large and tunable valley polarization in MoTe2. Thus, Mo0.75V0.25Te2 can be considered a promising 2D platform for tunable valleytronic device applications, such as transistors and sensors.

The quantum spin Hall effect (QSHE) has attracted widespread attention due to its dissipationless transport, which is protected by non-trivial topological invariants and helical edge states. Because even weak magnetic disorder can destroy the stability of topological quantum states, current research on the QSHE has primarily focused on non-magnetic materials. In this work, we extend the research scope of the QSHE to altermagnets. We establish the relevant symmetry constraints and identify all magnetic point groups that can realize the altermagnetic QSHE. Symmetry analysis reveals that pronounced spin-valley locking or spin-valley-layer locking universally exists in these systems. The concerted interaction between band inversion and spin-valley locking collectively gives rise to the helical edge states. Using first-principles calculations and theoretical models, we demonstrate that monolayer Nb2SeTeO exhibits an altermagnetic QSHE characterized by spin-valley locking, while bilayer Hf3Se3Te2 manifests an altermagnetic QSHE featuring spin-valley-layer locking. This work clarifies the intrinsic symmetry correlation between altermagnetism and quantum spin Hall topological phases, providing a brand-new theoretical perspective and research platform for exploring magnetic topological systems and developing next-generation spintronic devices

Interlayer interactions in layered materials are often assumed to transfer from the bilayer to the bulk, but this assumption can fail when chemically active out-of-plane orbitals participate in bonding. We combine diffusion quantum Monte Carlo (DMC) and density functional theory (DFT) to determine how interlayer coupling evolves in arsenene multilayers. DMC shows that bulk gray arsenic is compact, whereas the corresponding few-layer structures remain at substantially larger interlayer separations despite sharing the same nominal A$_{1}$B$_{-1}$ adjacent-layer registry. Registry alone therefore does not determine the bonding regime; thickness and coordination reshape the interlayer interaction. Among the tested functionals, SCAN+rVV10 most closely reproduces DMC equilibrium separations and stacking energetics. Using the DMC-benchmarked SCAN+rVV10 calculations, we predict a thickness-driven stacking sequence from A$_{1}$A$_{1}$ to A$_{1}$B$_{1}$ and finally bulk-like A$_{1}$B$_{-1}$. The structural crossover coincides with a stacking-dependent DFT band-gap collapse driven by enhanced interlayer As p$_{z}$ hybridization.