2732 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 review outlines critical strategies for achieving circularity in perovskite-based tandem photovoltaics—addressing material scarcity, recycling protocols, lead safety, and policy frameworks—to ensure their sustainable, terawatt-scale deployment alongside crystalline silicon technologies.
This paper theoretically demonstrates that the magnetic Weyl semimetal Co3Sn2S2 exhibits a switchable surface linear photogalvanic effect driven by extrinsic contributions from Fermi-arc states, which can be controlled by magnetization flipping and offers a promising platform for symmetry-controlled optoelectronic applications.
This paper presents a causal and energetic neural-network framework for learning history-dependent constitutive laws that ensures thermodynamic consistency, stability, and solution existence while demonstrating that learned internal variables are unique up to a linear transform, achieving a 2% relative error in predicting the response of a polycrystalline magnesium unit cell.
By combining machine learning screening with first-principles calculations, this study identifies the AuCrPS monolayer as a promising 2D van der Waals multiferroic candidate that enables non-destructive four-state nonvolatile memory through the intrinsic coupling of its ferroelectric polarization and ferromagnetic order via the bulk photovoltaic effect.
This paper introduces a "projected-mass" criterion for compensated magnetic topology, demonstrating that the exchange mass projected onto specific sectors—not spin splitting alone—defines Chern matter and enabling a two-channel diagnostic to distinguish hidden compensated Hall responses from additive altermagnetic quantum anomalous Hall phases.
This paper introduces a non-variational, deterministic algorithm for constructing Maximally Localized Wannier Functions by unifying gauge smoothing with the projected position operator eigenvalue problem through discrete adiabatic transport, thereby eliminating the need for iterative spread minimization while revealing the geometric origin of mesh-dependent spread scaling in systems like graphene.
Using first-principles calculations, this study demonstrates that the spin and orbital Hall conductivities of left- and right-handed trigonal selenium and tellurium exhibit opposite signs due to mirror-symmetry-induced antisymmetry in their Berry curvature, thereby establishing a direct link between measurable transport signals and structural chirality.
Based on first-principles calculations, this study identifies CaAgBi as a tunable Dirac-Weyl semimetal hosting distinct type-I and type-II Weyl points that can be manipulated through alloy engineering and strain to control their positions and annihilation for topological spintronic applications.
This study investigates the kinetic effects of thermal stimuli on Pluronic F127 solutions, revealing that heating and cooling rates significantly influence micellization temperatures and induce a novel, transient multi-step phase transition pathway characterized by metastable states and evolving microstructural order, which is successfully captured by a comprehensive mathematical model and phase diagram.
This paper introduces Automat, an autoresearch framework where an AI agent autonomously designs and iteratively refines chemically interpretable composition-based descriptors for materials property prediction, successfully outperforming established baselines in predicting band gaps and Curie temperatures while highlighting current limitations in search strategies and complexity control.