2756 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.
This paper introduces SLayerGen, a novel generative model that unifies the creation of both bulk crystals and diperiodic materials (such as 2D monolayers) by enforcing invariance to all space and layer groups through a hybrid architecture of autoregressive lattice sampling and equivariant diffusion, while also providing new datasets and metrics to advance the discovery of these previously underrepresented material systems.
This paper introduces neural operators as a powerful, discretization-independent tool for overcoming challenges in multiscale modeling of materials, demonstrating their effectiveness through three selected examples.
This paper reveals that injection and shift currents, traditionally viewed as distinct nonlinear optical responses, become equivalent in systems with linear electronic dispersion (such as Dirac and Weyl semimetals) because both are governed by the same interband quantum-geometric dipole, establishing a unified framework for interpreting these phenomena.
This paper introduces a novel thermodynamic framework that analyzes the interplay between structural transitions and spin-exchange parameters to successfully decipher complex magneto-structural phase transitions and extract characteristic temperatures in Ni-Mn-Cu-Ga Heusler alloys using standard magnetization data.
CrystalREPA is a plug-and-play framework that enhances the stability, validity, and fidelity of generated crystals by aligning generative model representations with frozen universal machine learning interatomic potentials (MLIPs) through a contrastive objective, revealing that an MLIP's effectiveness for transfer depends more on its representation distinguishability than its standard accuracy benchmarks.
This paper presents an exact analytical solution for an ideal one-dimensional electron gas confined in an anisotropic oscillator-shaped quantum wire with position-dependent effective mass, deriving wavefunctions and energy spectra via both canonical and non-canonical approaches using Laguerre and Gegenbauer polynomials.
This paper systematically evaluates nine MACE-based machine-learned interatomic potentials, demonstrating that while from-scratch models require specific high-energy training configurations to reduce errors, fine-tuning large foundation models offers superior transferability and accuracy across diverse catalytic reactions and out-of-distribution scenarios.
This study reveals that the stacking sequence (AA, AB, or ABC) in layered Sc₂Si₂Te₆ significantly modulates its electronic band degeneracy and lattice thermal conductivity, ultimately determining that the ABC and AB structures offer superior thermoelectric performance compared to the AA structure.
This study reveals that nitrogen implanted into preferentially forms molecular configurations rather than acting as substitutional acceptors, providing a microscopic explanation for the long-standing failure of nitrogen-based -type doping in this wide-band-gap semiconductor.
This study demonstrates that sliding-tunable van der Waals heterostructures composed of chalcogen-terminated MXenes and CrBr3 exhibit giant Rashba splitting and enhanced nonlinear Berry-phase responses, where mechanical sliding and magnetic proximity coupling synergistically drive emergent quantum anomalous Hall phases and valley-selective transport.