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 3
Excitons in solid molecular hydrogen from quantum Monte Carlo
We use quantum Monte Carlo (QMC) methods to investigate the lowest neutral excitation of solid molecular hydrogen in phase-I under increasing compression, with emphasis on structural and response…Does System Size Matter in Uncertainty Quantification for Machine Learning Interatomic Potentials?
Machine-learning interatomic potentials (MLIPs) commonly use uncertainty estimates to flag out-of-distribution (OOD) configurations and select structures for active learning.Cubic-Zn(1-x)Mn(x)Te optical modes across the phonon and phonon-polariton regimes
Alloying offers a convenient route to tune the physical properties of semiconductors, at the price, however, of local lattice/bond distortions needed to accommodate the lattice mismatch.Micromagnetic Lagrangian and Hamiltonian Normal-Mode Formulation of Cavity Magnon-Photon Interactions
We investigate magnon-photon interactions in a resonant cavity starting from the coupled Maxwell and Landau-Lifshitz equations linearized about an arbitrary micromagnetic ground state.Electric field control of anisotropic spin traffic between spin-filtering and magnon-scattering in a layered antiferromagnet
Layered A-type antiferromagnetic (AFM) semiconductors offer a unique platform for spintronic devices by combining robust magnetism with gate-tunable semiconducting properties.Framework-Conditional Open Materials Generation
Theoretical predictions of novel materials rely on a variety of techniques, from data-mining-based ion substitution methods to fully de novo generative models.ML-COHP: predicting bond descriptors using latent space of a universal interatomic potentials
The integrated crystal orbital Hamilton population (ICOHP) is the standard measure of bond strength in a DFT calculation, but every structure needs its own DFT single point and LOBSTER projection…Autonomous Quantum Measurement with Agentic AI
Quantum materials exhibit diverse electronic and functional phenomena, but their characterization requires extensive experimental expertise, iterative decision-making and accumulated laboratory…Collective sliding relaxor ferroelectricity across thousand-layer van der Waals superstructures
Relaxor ferroelectrics are among the most important electromechanical materials owing to their exceptionally large electrostrictive responses.HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks
Transition-metal-based batteries rely on critical elements such as lithium, cobalt, and nickel.Prospector: learning a compact graph traversal policy for scientific reasoning
Scientific reasoning in specialized engineering fields depends on knowledge scattered across the literature, often proprietary and absent from frontier LLM training data.Study of Multilayer TiNx/Ti/TiNx Films Formed In Situ for High-Efficiency Integrated Microheaters
A technology for the in situ formation of a three-layer TiN$_x$/Ti/TiN$_x$ functional material within a single vacuum cycle is presented, together with the results of investigations of its…Magnetic crystal generation with site-resolved magnetic moments via Gaussian-mixture retargeting and finite-budget yield prediction
We introduce GMMag, a workflow for generating crystal structures together with their site-resolved magnetic moments, built on a conditional variational autoencoder with a graph neural network…Dynamical quantum simulations as laboratories for quantum information
Dynamical quantum simulations are commonly designed to access a particular response function or correlation function.MoireKP: An Automated Workflow for Symmetry-Constrained Moiré Continuum Models
First-principles Hamiltonians contain the electronic information needed to study moiré materials, but their size makes subsequent calculations costly.Interpretable neural equation learning for high-dimensional materials data
Equation learning seeks to discover compact, interpretable equations from data using various machine-learning techniques.Growth Phase Map of α and \b{eta} Tantalum as a Function of Growth Temperature and Oxygen Partial Pressure
Tantalum thin films are used in low-loss superconducting quantum circuits, yet the growth conditions governing selection of $α$-Ta(110), $α$-Ta(111), and metastable $β$-Ta on c-plane sapphire remain…Amorphization-Depth-Dependent Bismuth Redistribution and Activation in Silicon for Quantum Devices
Near-surface bismuth donors are promising for silicon quantum devices because of their rich electron-nuclear spin structure and long spin coherence, but their electrical activation is strongly…Symmetry-resolved fermionic correlations in iron-oxide ground spaces
Symmetries of quantum systems provide a natural way to organize the eigenspaces of many-body Hamiltonians.Physical structure as an organizing principle for universal Hamiltonian learning
Density functional theory (DFT) gives the electronic structure of materials, but its cost limits large-supercell calculations and materials screening.