Condensed Matter · Physics & Astronomy
Materials Science
The newest papers in Materials Science that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
269 papers, from 31 August 2026 to 5 November 2026: 195 arXiv preprints and 74 OpenAlex records.
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Newest papers
Dark-State Coherent Control of Excitons in TMD Monolayers: A Reduced Intravalley Lindblad Framework for WSe$_2$
Dark-state coherence in driven three-level systems underlies electromagnetically induced transparency (EIT) and stimulated Raman adiabatic passage (STIRAP).Spectral symmetry fingerprints in equivariant representations
Symmetry governs the organization of physical structures, while symmetry breaking reveals their transformations.Equivariant Deep Learning in Direct-Product Space for Electronic-Structure Hamiltonian Prediction
Most existing equivariant architectures for Hamiltonian prediction propagate features in direct-sum spaces of irreducible spherical tensors, whereas the target Hamiltonian is naturally an operator…Continuum Photonic Axion Insulators
Here, I study the conditions for realizing an axion topology in a homogeneous inversion-symmetric electromagnetic continuum, in the absence of a crystalline lattice.A Passive Intelligent Metasurface: A Network Model
Intelligent metasurfaces enable advanced wave manipulation, including negative refraction, focusing, beam splitting and beam steering, with significant potential for practical applications.Towards Multiscale Molecular Representation Learning through Geometry-Based Fragmentation for Polymer Property Prediction
It is well established that molecular substructures can contribute unequally to chemical and physical properties, with specific motifs or side chains often playing dominant roles.Helium States in a CAS(2,2) Subspace Inspired by Quantum Photonics
We present a four-mode Fock-state photonics implementation of the variational quantum deflation (VQD) algorithm to calculate the ground-state and excited-state energies for the neutral helium atom…Evolutionary Design of Interpretable Spatial Descriptors for Structure-Property Relationship Discovery
Structure property prediction is a classical task in machine learning, ranging from molecular activity and materials properties to local functional responses measured in microscopy.Thermally Conditioned Time-Series Forecasting of Grain Growth in Polycrystalline Materials
Grain growth during heat treatment strongly affects the microstructure of polycrystalline materials and, consequently, their mechanical properties.Data-Driven Modeling of Molten Chloride Corrosion Using Static Test Data
Molten chloride corrosion has been studied for decades, yet reliable prediction across different alloys, salts, and test conditions remains difficult.Thermally enhanced magnon-phonon resonance in van der Waals antiferromagnet FePS3
Strong coupling between collective excitations provides a route to controlling quantum states of matter, yet its evolution under thermal fluctuations remains poorly understood.Surrogate Models for Stochastic Al-Cu Phase-Field Solidification
Phase-field models resolve microstructural evolution during rapid alloy solidification, but their computational cost limits long trajectories, stochastic ensembles, and studies across process…Band structure of two-dimensional graphene and of one-dimensional nanoribbons via variational quantum algorithms: from energies to wavefunction quality
Simulating quantum many-body systems classically encounters exponential bottlenecks, which quantum devices circumvent by natively encoding wavefunctions.Toward a Quantum Information Foundation for Neutron Scattering
Neutron scattering is a central probe of quantum materials, yet what quantum information can, in principle, be extracted from a scattering experiment remains unclear.Exact Mirror-Parity Selection Rule for Electron-Phonon Coupling in Two-Dimensional Crystals
The electron--phonon (e-ph) interaction underlies conventional superconductivity and plays a central role in transport and other many-body phenomena in solids.SteelAgent: LLM-Orchestrated, Physics-Informed Steel Property Prediction with Audited Generalization and Calibrated Uncertainty
Steel property models can perform well on random test splits even when they extrapolate poorly to unfamiliar alloys.Machine-Learned Interatomic Potential for the Mo-S-Al-O System
We develop a machine-learned neuroevolution potential (NEP) for the Mo-S-Al-O system, targeting MoS$_2$ heteroepitaxy on c-plane sapphire, $α$-Al$_2$O$_3$(0001).Organizational Learning in Materials Discovery Campaigns: Which Databases, Calculations, and Agent Teams Pay Off
More databases can make a materials campaign worse.Site-orbit masked twin-branch Transformer for symmetry-constrained crystal generation
Generative models for crystal structure prediction (CSP) have expanded the search space for novel materials and improved screening efficiency.PaddleMaterials: An End-to-End Deep Learning Toolkit for Materials Science
Machine learning supports materials design, atomistic simulation and experimental characterization, yet models and workflows remain fragmented.