4066 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 develops a machine-learning interatomic potential framework combining GAP and MACE models with lattice-dynamical and non-equilibrium molecular dynamics simulations to provide a more accurate and internally consistent description of phonon-mediated thermal transport and grain boundary resistance in nanocrystalline silicon compared to classical potentials.
This paper presents and benchmarks the implementation of both direct (ScaLAPACK/ELPA) and iterative (SLEPc) solvers for Hermitian and pseudo-Hermitian Bethe-Salpeter equations within the Yambo code, demonstrating the feasibility of handling dense matrices of up to elements on CPU and GPU clusters.
The paper introduces QR-code, an open-source program that utilizes first-principles calculations from Quantum ESPRESSO and EPW to compute single-resonance, double-resonance, and defect-induced Raman spectra, providing tutorials and validation through graphene examples.
This paper introduces Dopyqo, an open-source framework that enhances Bader charge analysis for both weakly and strongly correlated periodic systems by combining classical DFT calculations with many-body post-processing via the variational quantum eigensolver, demonstrating significant accuracy improvements over standard DFT for transition metal oxides.
This study employs multiscale modeling with machine-learning potentials to reveal how grain boundaries and anion size govern ion transport in argyrodite solid electrolytes, offering critical insights for the microstructural design of high-performance all-solid-state batteries.
This paper presents a quantum-path electron interferometer based on time- and angle-resolved photoemission spectroscopy that successfully reconstructs the previously inaccessible phase of electronic wavefunctions, demonstrated by resolving the phase jumps and helicity of Dirac states in a topological insulator.
This study demonstrates that high-flux soft X-ray synchrotron radiation can partially restore the stoichiometric chemical and electronic properties of degraded, hygroscopic VI3 crystals by reversing surface contamination and vanadate formation.
This study demonstrates that direct-write laser annealing can pattern phase gradients in tungsten thin films to create spin-orbit torque channels capable of switching CoFeB magnetization without external magnetic fields, offering a new strategy for designing efficient spintronic devices.
This paper investigates the growth kinetics and optical properties of fractional-monolayer GaN/AlN quantum wells grown by plasma-activated molecular beam epitaxy, proposing a phenomenological model that links their emission characteristics to specific growth mechanisms and demonstrating their potential as powerful ultraviolet-C emitters.
This paper introduces QHFlow2, a state-of-the-art machine learning Hamiltonian model that significantly outperforms existing methods in energy and force prediction accuracy by directly evaluating predicted Hamiltonians, achieving NequIP-level force precision and up to 20-fold energy error reductions on standard benchmarks.