4853 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 introduces a band-avoiding occupation-constrained density functional theory (ba-occ-DFT) method that overcomes the zero band gap problem in narrow-gap semiconductors like indium arsenide (InAs), enabling accurate predictions of atomic defect levels despite the failure of standard DFT to reproduce the experimental band gap.
This paper introduces a rapid, surrogate-free Bayesian inversion framework for neutron reflectometry that leverages exact gradients to enable highly efficient Hamiltonian Monte-Carlo and Variational Inference methods, offering state-of-the-art uncertainty quantification speeds and robustness for complex samples without sacrificing physical intuition.
The paper proposes the Coordination Polyhedron Graph Network (CPGN), a multi-scale graph neural network that explicitly models atomic, bond, and coordination-polyhedron levels with bidirectional cross-attention to achieve state-of-the-art accuracy in predicting crystal properties by capturing fundamental structural units often missed by existing models.
This paper presents a flexible, scalable lattice-based kinetic Monte Carlo framework for simulating GaN molecular beam epitaxy that integrates predefined activation-energy catalogs with adaptive, machine-learned on-the-fly barrier evaluations to accurately model complex growth phenomena such as island formation, Ostwald ripening, and temperature-driven island walking.
Mandala is a modular, E(3)-equivariant graph neural network framework that bridges the gap between machine learning and electronic structure calculations by learning block-sparse quantum operators to directly predict electronic observables like band structures and densities of states, thereby complementing traditional machine learning interatomic potentials.
This paper introduces a parameter-free, first-principles framework based on nonperturbative Green-Kubo dynamics that accurately predicts charge transport in molecular semiconductors across diverse regimes, overturns the prevailing microscopic mechanism for DNTT by attributing transient localization to correlated on-site disorder, and identifies the phenacene family as a promising direction for high-mobility materials.
This paper presents an integrated experimental framework combining IV, LBIC, and bias-dependent KPFM to unambiguously identify and characterize Schottky versus tunnel contact barriers in few-layer MoS2 devices, demonstrating that while thermal annealing reduces total resistance, contact barriers remain the dominant limiting factor.
This review unifies the geometric language of SU(2) degrees of freedom across diverse quantum systems to demonstrate how symmetry-protected wavefunction continuity and topological winding serve as a practical design framework for engineering next-generation memory, switching, and sensing technologies.
This paper presents an extended angular creep model that incorporates dispersive domain wall stiffness and chiral damping to accurately describe the anisotropic expansion of magnetic bubble domains, thereby enhancing the quantitative extraction of Dzyaloshinskii-Moriya interaction and chiral dynamical effects.
The paper introduces Transformer Atomic Cluster Expansion (TRACE), an energy-conserving machine-learning architecture that successfully unifies the modeling of diverse physical phenomena—including polymorphic phase transitions, liquid structures, and chemical reactivity—by combining atomic cluster expansion correlations with local multihead cross-attention.