4853 papers
Condensed matter physics and materials science form a dynamic partnership, exploring how the collective behavior of atoms gives rise to the unique properties of solids and liquids. This field bridges the gap between fundamental quantum mechanics and the practical engineering of everything from flexible electronics to superconductors, turning abstract theories into tangible innovations that shape our daily lives.
At Gist.Science, we process every new preprint in this category directly from arXiv to make these complex discoveries accessible to everyone. Our team generates both plain-language overviews and detailed technical summaries for each paper, ensuring that researchers, students, and curious minds alike can grasp the latest breakthroughs without getting lost in dense jargon.
Below are the latest papers in condensed matter and materials science, organized by their most recent publication dates.
The paper introduces Catalyst Diffusion Transformer (CatDiT), a unified generative framework that efficiently designs novel and valid heterogeneous catalysts by simultaneously conditioning on adsorbate type, binding energy, and catalyst class, successfully demonstrating its capability to enrich candidate pools for targeted reactions like nitrogen reduction.
This paper introduces a graph neural network approach that successfully aligns heterogeneous DFT datasets by predicting cross-functional energy residuals, thereby upgrading legacy PBE calculations to high-precision r2SCAN accuracy and enabling the integration of multi-source data for robust materials AI models.
This paper presents a data-efficient neural-vision system that automates crystallographic alignment during CdTe molecular beam epitaxy growth by training on only 15 RHEED patterns and leveraging physics-aware postprocessing to replace manual inspection.
This paper presents an amortized, likelihood-free framework combining Gaussian process surrogates and Conditional Flow Matching to efficiently estimate material constitutive parameters from multimodal Small Punch Test data, demonstrating that integrating Digital Image Correlation fields with force-displacement curves significantly improves posterior precision compared to using global response data alone.
This paper introduces a general phase-field model for interfacial chemical equilibrium that utilizes auxiliary non-conserved variables to describe composition differences, unifying the WBM and KKS models as limiting cases while enabling direct integration of thermodynamic databases to solve complex problems like interdiffusion between ordered phases with non-overlapping composition ranges.
This study proposes and validates via DFT calculations a BaMnO|KTaO heterostructure that successfully induces a strong Rashba spin-orbit interaction in the antiferromagnetic BaMnO layer through proximity to KTaO, offering a promising pathway for advancing antiferromagnetic spintronics.
This paper proposes the DARK-ROSE experiment, which utilizes high-quality-factor millimeter-sized spherical dielectric resonators to enhance axion-mediated photon-to-photon transitions by tailoring Mie resonances to match expected axion frequencies, thereby offering a new pathway to detect axion dark matter.
This study identifies and experimentally validates a robust, room-temperature orbital-selective flat-band mechanism in van der Waals transition-metal oxychlorides (NbOCl2 and TaOCl2), arising from specific orbital hybridization and Peierls dimerization, thereby establishing a new design principle for realizing exotic quantum phases.
This paper presents NanoBTE, a deterministic finite-volume solver that efficiently solves the non-gray phonon Boltzmann transport equation for complex nanoscale geometries by integrating advanced boundary conditions, band-resolved properties, and hybrid CPU-GPU parallelization with synthetic iterative schemes to accelerate convergence in multiscale heat transport simulations.
This paper proposes a materials property hierarchy and evidence-based framework to address the challenges of substantiating novelty in AI-driven materials discovery, arguing that current limitations in multimodal data integration and benchmarking must be overcome through community-wide standards to enable the design of experimentally realizable materials.