🔬 Category

cond-mat.mtrl-sci

4119 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.

🔬 condensed matter

BCS-BEC crossover driven by small Fermi pockets of a high-Tc cuprate superconductor

Using angle-resolved photoemission spectroscopy and quantum oscillations on the four-layer cuprate Ba2Ca3Cu4O8(F,O)2, this study demonstrates the coexistence of small Fermi pockets and large superconducting gaps, thereby providing microscopic evidence for a BCS-BEC crossover that emerges unexpectedly with increasing carrier density and supports the d-wave pairing mechanism in doped AF-Mott insulators.

Junhyeok Jeong, Yamato Enomoto, Yoshimitsu Kohama, Tomotaka Nakayama, Kotaro Ando, Kifu Kurokawa, Soonsang Huh, Zhuo Yan (…)2026-06-05
🔬 materials science

Endowing variational phase-field fracture models with custom strength criteria

This paper proposes a novel variational phase-field fracture framework that incorporates arbitrary elastic domains and custom strength criteria by introducing a state-dependent dissipation potential, thereby preserving the variational structure while allowing independent control over elastic degradation and crack nucleation under multiaxial stress states.

Roberto Alessi, Matteo Brunetti, Roshan Udaram Patil, Jacinto Ulloa2026-06-05
🔬 materials science

PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design

PolyGraphPy is an open-source Python framework that integrates atomistic simulations with machine learning, including Bayesian Graph Neural Networks and generative models, to automate data generation, predict polymer properties with uncertainty quantification, and enable the de novo design of targeted polymer molecules.

João G. C. S. Duarte, Shruti Venkatram, Morgan Cencer, Traian Dumitric\va, Ketson R. M. dos Santos2026-06-05
🔬 materials science

Low-loss Nb on Si superconducting resonators from a dual-use spintronics deposition chamber and with acid-free post-processing

This paper demonstrates that high-quality, low-loss niobium superconducting resonators can be fabricated in a dual-use chamber shared with magnetic materials by employing an acid-free resist strip process that achieves internal quality factors near one million, thereby enabling the integration of superconducting and magnetic systems without compromising device performance.

Maciej W. Olszewski, Jadrien T. Paustian, Tathagata Banerjee, Haoran Lu, Jorge L. Ramirez, Nhi Nguyen, Kiichi Okubo, Roh (…)2026-06-04