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 11
A property-registry contract for retrieve-or-refuse thermal-mechanical lattice search
Early thermal-mechanical lattice requirements are knowledge-intensive and often jointly unsatisfiable: an engineer asks for a cell that is light, stiff, laterally conducting and cheap, and no cell…Prescreening Point Defects in Semiconductors With Machine Learning
High-throughput calculations using density-functional theory (DFT) are commonly used to explore point defects for applications in power electronics and quantum technologies.Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning
Understanding and inferring dislocation microstructures from diffraction patterns remains an open challenge in materials characterization, as diffraction measurements provide only indirect…Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design
The inverse design of crystalline materials ultimately seeks structures with desired physical properties.The Hausdorff dimension of the spectrum of the Thue-Morse Hamiltonian
In 2015, Liu, Qu (Commun.Extreme-Scale Linear-Scaling Kohn-Sham DFT at 100 Million Atoms: Bridging Quantum Simulations and Experiments
Kohn-Sham density functional theory (DFT) remains the workhorse of ab initio materials simulation, yet cubic computational and quadratic memory scaling have confined calculations to a few hundred to…Mined from Scientific Literature: Process Schemas for Atomic Layer Deposition and Etching in Materials Science
Atomic layer deposition (ALD) and atomic layer etching (ALE) are reported heterogeneously across experimental and simulation literature in materials science, hindering comparison and…Physics as the label for measuring and correcting materials reasoning in multimodal models
Vision-language and language models increasingly interpret materials data, yet benchmarks report that they hallucinate invalid properties and violate physical law.Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation
Designing polymers with targeted properties requires navigating vast chemical spaces from limited labeled data.uFlowCSP: Crystal Structure Prediction using Mean flow generative models
Crystal structure prediction (CSP) is fundamental to computational materials discovery.An Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach…Symmetry-aware super-resolution of crystal orientation maps via invariant latent-space learning
Crystal-orientation maps are physical fields defined only up to crystal symmetry; electron backscatter diffraction (EBSD) resolves them experimentally, but acquisition-time constraints limit spatial…Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators
Machine learning surrogates based on neural operators have shown broad applicability in solving forward PDE problems.MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials
Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations.Balanced and broadly-normed meta-generalized gradient approximation
Conventional semi-local density functional approximations for the exchange-correlation energy systematically underestimate fundamental band gaps.Quantum-accurate atomistic modeling of enzyme catalysis using a machine learned potential
Electronic rearrangements associated with bond forming/breaking in catalytic enzymes require quantum mechanical (QM) treatment beyond classical molecular mechanics (MM).Human-agent discovery of reconfigurable in-plane ferroelectric superdomain control
Automated experimentation is most effective when the observables, available actions, and objective are defined before the experiment starts, as is the case for Bayesian optimization.Online Surrogate Repair: Decoupling High-Fidelity Feedback from Search Length in Closed-Loop Discovery
Closed-loop AI scientists can generate candidate designs at low marginal computational cost, whereas reliable feedback may require wet-lab synthesis, characterization, or high-fidelity computation.The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry
This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping materials science discovery.Demonstration of room-temperature, magnetic-field-free, and ultralow-power spin-orbit torque logic device based on composition-uniform van der Waals magnet Fe3GaTe2
Van der Waals magnets have received blooming interest in material science and spintronics for the advantages of low magnetization, flexible stacking, strong tunability, and dangling-bond-free…