2692 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 demonstrates that sub-bandgap AlN photodetectors, engineered with specific dopant designs and contact structures to create a narrow space charge region, achieve non-saturating, linear responses to ultra-bright blue light and elevated temperatures by leveraging deep-level defect-mediated photoresponse, thereby enabling reliable sensing in extreme industrial and aerospace environments.
This paper introduces an agent-guided multi-fidelity learning framework that employs a structural agent to diagnose numerical instabilities in GW-Bethe-Salpeter calculations and applies machine learning corrections to accurately predict quasiparticle and excitonic properties in strained MoS2-WS2 bilayers, demonstrating that explicit detection of numerical fragility is essential for reliable surrogate modeling of excited-state materials.
This study demonstrates that phase-pure, superconducting platinum silicide (PtSi) thin films with stable microstructures and consistent properties can be rapidly formed on silicon via thermal processing at 600°C, establishing a robust fabrication window for CMOS-compatible quantum devices while identifying interfacial roughening as an intrinsic consequence of the phase conversion rather than thermal degradation.
This study utilizes advanced ab initio calculations and symmetry-breaking analyses to demonstrate that excitonic insulator states spontaneously form in monolayer and bilayer 1T-HfTe2 due to negative exciton energies, while remaining absent in trilayer and bulk forms, with theoretical predictions aligning well with experimental observations.
This paper investigates the post-annealing crystallization behavior of RF-sputtered Yttrium Iron Garnet (YIG) thin films on patterned and non-patterned Si/SiO2 substrates to establish a fabrication route for suspended magnonic devices, while noting that further stoichiometry optimization is needed for fully released structures.
This study demonstrates the wafer-scale fabrication of highly uniform, high-voltage lateral -GaO MOSFETs on a 2-inch MOCVD-grown epitaxial wafer, achieving breakdown voltages exceeding 3 kV and excellent device consistency suitable for next-generation power applications.
This paper demonstrates that metastable phases, typically suppressed in equilibrium, can be tracked and characterized as regions in the complex plane of thermal fields delineated by Lee-Yang zeros, offering a new framework for understanding and engineering non-equilibrium collective states in periodically driven systems.
This paper presents a Gaussian-process surrogate model trained on transfer-matrix-method simulations that accelerates the prediction of GaAs/AlGaAs distributed Bragg reflector spectra by approximately 70 times compared to traditional methods, though it underperforms a Random Forest baseline in accuracy while providing well-calibrated uncertainty estimates.
This study demonstrates the highly chemoselective and stereospecific surface synthesis of tetraaza[4]radialenes via a [1+1+1+1] cycloaddition of isocyanides on Au(111), followed by their long-range 2D crystallization into homochiral structures driven by enantioselective molecular recognition.
This paper demonstrates that an active learning framework based on information-matching can efficiently generate bespoke interatomic potentials tailored for predicting metal plastic strength by targeting correlated intermediate quantities, while also highlighting the necessity of post hoc uncertainty inflation to address residual model errors.