2832 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.
First-principles rt-TDDFT calculations on monolayer GeS reveal that Coulomb scattering generates ballistic photocurrents comparable to shift currents, identifying a previously overlooked mechanism for the bulk photovoltaic effect.
Using a first-principles-based spin-spiral approach and atomistic spin models, this study predicts the emergence of diverse topological spin textures, including Néel-type skyrmions and bimerons, in an all-magnetic FeGeTe/CrGeTe van der Waals heterostructure driven by interfacial Dzyaloshinskii-Moriya interactions and geometric exchange frustration.
This study demonstrates that alloying monolayer MoWSe semiconductors enables the engineering of extremely high excitonic -factors (up to -10) through non-trivial band structure modifications, as confirmed by both magneto-optical spectroscopy and first-principles calculations.
This paper demonstrates that modeling secondary electron counts as a mixture distribution, rather than a simple convolution, enables a maximum likelihood estimator that achieves significant sub-pixel edge localization accuracy—reducing root mean-squared error by approximately five-fold compared to conventional methods in both simulations and real helium ion microscopy datasets.
This study investigates air-stable, anisotropic CrSBr from monolayer to trilayer, revealing a unique magneto-excitonic duality of Frenkel- and Wannier-Mott-like excitons and demonstrating that while mono- and trilayer systems exhibit similar magnetic responses, the bilayer displays a distinct signature due to different origins of its low-lying excitonic species.
This paper develops a symmetry-adapted multipolar theory for noncentrosymmetric crystals in the strong spin-orbit coupling limit, revealing that multipolar interactions for reshape Fermi surfaces and create distinct total-angular-momentum textures that lead to nonmonotonic, enhanced Edelstein effects in heavy-element materials.
This paper introduces a unified geometric framework based on a generalized time-dependent quantum geometric tensor to derive compact expressions, sum rules, and fundamental bounds for optical, thermoelectric, and thermal transport in clean band insulators, revealing that geometry-driven effects persist even in topologically trivial systems.
This study utilizes first-principles DFT calculations to demonstrate that endohedral doping of the recently synthesized two-dimensional fullerene network (qHPC) with nitrogen, cerium, or strontium preserves its semiconducting nature while introducing localized states that redshift optical absorption into the visible spectrum, thereby highlighting its potential for optoelectronic and light-harvesting applications.
This paper demonstrates that doping collinear van der Waals antiferromagnets (specifically MnNiPS) with transition metal ions like Ni provides a versatile strategy to engineer and significantly enhance ultrafast, helicity-controlled photomagnetic responses for next-generation spintronic devices.
This paper introduces Flexible Cutoff Learning (FCL), a method that trains machine learning interatomic potentials with randomly sampled cutoff radii to enable post-training optimization of per-atom cutoffs, thereby significantly reducing computational costs for specific applications without requiring retraining.