3992 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 increasing LiTFSI concentration in DMC electrolytes restructures the solvation shell to promote anion-rich contact ion pairs, which accelerates early SEI nucleation and yields a denser, inorganic-rich interphase compared to conventional electrolytes.
This paper proposes BDIP-Net, a machine-learning framework that combines a cost-effective MatterSim-D3 structural optimization workflow with a dual-interaction graph neural network to efficiently generate DFT-quality bilayer structures and accurately predict their properties by explicitly modeling both intra-layer and inter-layer interactions.
This paper introduces NanoEQL, a fully interpretable white-box neural network that deciphers nanocrystal size determination mechanisms by modeling synthesis outcomes as a linear equation of crystallization, growth, and external potential capabilities, thereby overcoming the black-box limitations of traditional deep learning in chemical reaction analysis.
This paper demonstrates that physics-informed symbolic regression, utilizing the Rigid Unit Packing Fraction descriptor, successfully derives an interpretable closed-form model for predicting the glass transition temperature of alkali borate glasses with high accuracy while explicitly capturing the physical interplay between structural rigidity, dissociation energy, and network packing.
This paper demonstrates that improvements from machine-learned exchange-correlation functionals do not automatically transfer to density-functional tight binding due to fundamental incompatibilities between orbital-dependent operators and multiplicative potentials, often resulting in "anti-transfer" where band gaps worsen, while identifying specific structural components like on-site blocks and polarization shells that must be optimized to achieve successful parameterization.
This paper demonstrates that while orbital angular momentum (OAM) selection rules identify specific channels for exciting optically forbidden paraexcitons in CuO, efficient excitation ultimately requires structured light with a spatially localized intensity ring (approximately 6–7 Bohr radii) that matches the exciton's characteristic length scale, thereby establishing a dual framework of symmetry and spatial localization for engineering structured-light interactions.
This paper investigates superconductivity in various hydrides using advanced theoretical frameworks that incorporate anharmonicity and nonadiabaticity, introducing specific diagnostic metrics to determine when corrections beyond standard Migdal-Eliashberg theory are necessary to accurately predict critical temperatures.
This paper demonstrates that low-symmetry epsilon-near-zero (ENZ) conductors can exhibit driven time-crystalline behavior under optical pumping, where strong anomalous-velocity nonlinearities enable subwavelength nanoparticles to overcome dissipation, achieve parametric amplification, and even render probe extinction negative.
This first-principles study utilizes density functional theory to establish a predictive model for the tensile strength of -Ti alloys by linking electronic bonding metrics, specifically ICOHP, and short-range ordering to mechanical properties, thereby advancing the computational design of high-performance structural materials.
This paper establishes sputter-grown barium hexaferrite (BaM) thin films as a scalable, strain-free magnetic-insulator platform with intrinsic perpendicular anisotropy that enables efficient interfacial exchange coupling for proximity-engineered spintronic and topological devices, offering a practical alternative to rare-earth iron garnets.