🔬 Category

cond-mat.mtrl-sci

2898 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.

🔬 materials science

Magneto-optical properties of Group-IV--vacancy centers in diamond upon hydrostatic pressure

This study employs density functional theory and a novel Jahn-Teller framework to investigate the magneto-optical properties of Group-IV-vacancy centers in diamond under hydrostatic pressure up to 180 GPa, revealing that while spin-orbit splitting and zero-phonon-line energy increase with pressure, PbV(-) centers lose photostability beyond 32 GPa whereas SiV(-), GeV(-), and SnV(-) remain stable, alongside detailed characterizations of hyperfine interactions and spin coherence times across various temperature regimes.

Meysam Mohseni, Lukas Razinkovas, Vytautas Žalandauskas, Gergő Thiering, Adam Gali2026-02-12
🔬 materials science

Discovery of Hyperelastic Constitutive Laws from Experimental Data with EUCLID

This paper evaluates the EUCLID framework for the automated discovery of hyperelastic constitutive laws using experimental data from natural rubber specimens, comparing its performance against conventional parameter identification methods in terms of predictive accuracy, generalization to unseen geometries, and coverage of the material state space.

Arefeh Abbasi, Maurizio Ricci, Pietro Carrara, Moritz Flaschel, Siddhant Kumar, Sonia Marfia, Laura De Lorenzis2026-02-12
🔬 materials science

Anisotropic and isotropic elasticity and thermal transport in monolayer C24_{24} networks from machine-learning molecular dynamics

Using a machine-learned neuroevolution potential, this study demonstrates that the mechanical stiffness and thermal transport of monolayer C24\text{C}_{24} networks are governed by their bonding topology, where the quasi-tetragonal phase exhibits isotropic properties and the quasi-hexagonal phase shows pronounced anisotropy driven by low-frequency acoustic phonons.

Qing Li, Haikuan Dong, Penghua Ying, Zheyong Fan2026-02-12
🔬 applied physics

Resonant states and nuclear dynamics in solid-state systems: the case of silicon-hydrogen bond dissociation

This paper presents a comprehensive non-adiabatic theoretical framework using first-principles density functional theory and a partitioning scheme to elucidate the mechanism of silicon-hydrogen bond dissociation in solid-state systems, demonstrating how transient occupation of antibonding states drives nuclear wavepacket propagation and determining dissociation probabilities relevant to hot-carrier degradation in semiconductor devices.

Woncheol Lee, Mark E. Turiansky, Dominic Waldhör, Byounghak Lee, Tibor Grasser, Chris G. Van de Walle2026-02-12
🔬 materials science

Crystal Representation in the Reciprocal Space

To address the lack of one-to-one correspondence in traditional direct-space representations, this paper proposes a continuous, rotationally and translationally invariant 4D reciprocal space representation based on power spectra of orthogonal spherical harmonics and radial bases to better facilitate crystal structure determination and generative modeling.

Osman Goni Ridwan, Hongfei Xue, Youxing Chen, Harish Cherukuri, Qiang Zhu2026-02-12
🔬 materials science

diffpy.morph: Python tools for model independent comparisons between sets of 1D functions

`diffpy.morph` is an open-source Python package designed to reveal meaningful scientific insights from 1D spectra by applying "morphs" to datasets to remove uninteresting differences, such as experimental inconsistencies or thermal expansion, during model-independent comparisons.

Andrew Yang, Christopher L. Farrow, Pavol Juhás, Luis Kitsu Iglesias, Chia-Hao Liu, Samuel D. Marks, Vivian R. K. Wall (…)2026-02-12