4797 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 study demonstrates that epitaxial SrVO₃ thin films exhibit magnetoresistance signatures consistent with the orbital Hall effect, supported by density functional theory calculations and diffusive modeling that reveal a predominantly orbital response with a large orbital-to-spin Hall conductivity ratio, establishing narrow-band d¹ metallic oxides as a promising platform for orbital transport.
This paper validates a nonempirical, ab initio-informed electronic stopping model for large-scale atomistic simulations by demonstrating its accuracy in predicting trajectory-dependent energy losses through comparison with experimental ion transmission data.
This density functional theory study reveals that while pristine B12N12 nanocages only weakly physisorb phosgene, platinum doping significantly enhances binding affinity and enables spontaneous, reversible adsorption at room temperature, making Pt-doped B12N12 the most effective candidate for practical phosgene detection and regeneration.
This paper introduces a complete machine learning framework, featuring the novel AugNet model for augmentation occupancies and spin initialization, that satisfies all seven criteria for a reference-free electronic initializer, thereby accelerating end-to-end plane-wave DFT calculations for materials by up to 25% without requiring prior converged data.
By combining soft-X-ray angle-resolved photoemission spectroscopy with first-principles calculations, this study establishes that hybrid functionals and approximations accurately reproduce the multivalley electronic structure of bulk PbSe, whereas semi-local functionals fail, highlighting the critical importance of precise band structure modeling for predicting thermoelectric performance.
This study establishes ScAuAl as a strong-coupling, multigap superconductor with a transition temperature near 5 K by combining experimental measurements of specific heat and transport properties with first-principles Eliashberg calculations, revealing a large specific-heat jump, two distinct superconducting gaps, and a discrepancy between thermodynamic and resistive upper critical fields attributed to disorder at grain boundaries.
This paper introduces PAOFLOW 3.0, an open-source Python suite that automates the construction of pseudo-atomic-orbital Hamiltonians from plane-wave DFT calculations to enable cost-effective, high-throughput analysis of diverse electronic, transport, and topological properties, with new capabilities including VASP support, Hubbard corrections, and quantum transport simulations.
This paper investigates charge and thermoelectric transport in monolayer graphene with dilute non-Hermitian impurities, demonstrating that while gain enhances carrier lifetime and thermoelectric performance, absorption suppresses them, thereby establishing non-Hermitian scattering as a tunable mechanism for controlling graphene's energy-dependent relaxation and thermoelectric response.
This paper presents a unified Cartesian framework and the accompanying web tool MagSymMultipoles to systematically classify electric and magnetic multipolar orders in crystals, directly linking these symmetries to allowed physical property tensors for the intuitive analysis of materials like ferroelectrics, magnetoelectrics, and altermagnets.
This paper benchmarks uncertainty quantification strategies for machine learning interatomic potentials using argon coupled-cluster data and proposes a hierarchical Bayesian Gaussian process design that effectively balances accuracy, calibration, and error discrimination while explicitly dissecting uncertainty sources for reliable applications.