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, page 14
Switchable Magnetoelectric Transport in Graphene via a Van der Waals Multiferroic
Electric and magnetic control of transport properties at atomic interfaces is central to the development of next generation electronics and spintronics.Linear stability and viscoelastic response in weakly jammed frictional granular systems
Granular particles typically feature contact friction, which gives rise to various nontrivial properties of granular materials.Bounds on Shaping Partially Coherent Microwaves with Programmable Scattering Systems
We derive bounds on manipulating partially coherent microwaves with programmable scattering systems.Dual-Gradient Plasmonic qBIC Metasurface for Time-Resolved In Situ Optimization of Molecular Vibrational Sensing in Water
Metaphotonic platforms based on quasi-bound states in the continuum (qBICs), harnessing strong near-field enhancement and deeply subwavelength field confinement, provide a versatile framework for…Data and code for: Fermi-Level Metal-d Character of Group-6 Bis-Hexahapto Bilayer Graphene
Calculation, analysis and figure sources for arXiv:2609.00083, together with every machine-readable record the reported numbers are rebuilt from.Using microrheology to study dynamical heterogeneities
Dynamical heterogeneities are one of the hallmarks of supercooled liquids, and their properties and relevance have been studied with theory, simulations and experiments.Mixture of Polyconvex Neural Potentials for Parametric Hyperelasticity: Towards Foundation Material Models
Hyperelastic constitutive models enable modeling large deformations in elastic solids.Size-Dependent Growth Rates Amplify Infinitesimal Asymmetry in Nanocrystals
The kinetic Wulff construction predicts symmetric nonequilibrium shapes when crystallographically equivalent facets share a fixed growth rate.AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials
Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamics simulations with first-principles accuracy.