🔬 Category

cond-mat.mtrl-sci

2692 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.

Modeling phase separation in polymer-derived carbonitride ceramics through extended machine learning molecular dynamics

This study employs a machine learning interatomic potential trained on over 9,000 configurations to simulate large-scale molecular dynamics of silicon carbonitride systems, revealing that thermal treatment drives phase separation where defective carbon rings mediate the nucleation of graphene-like sheets within the amorphous matrix, thereby explaining the material's unique hybrid properties.

Fabien Mortier, Sylvian Cadars, Olivier Masson, Mauro Boero, Guido Ori, Yun Wang, Samuel Bernard, Assil Bouzid2026-05-21🔬 cond-mat.mtrl-sci

Dataset-aware entropy-maximized active learning for machine-learned interatomic potentials

This paper presents a dataset-aware, entropy-maximized active learning framework that combines local entropy-driven molecular dynamics with global information filtering to efficiently generate high-quality training data for machine-learned interatomic potentials, achieving significantly lower energy errors than random sampling across diverse chemical systems with minimal DFT-labeled structures.

Meiyan Wang, Rishi Rao, Li Zhu2026-05-21🔬 cond-mat.mtrl-sci

Superconducting PdTe Thin Film Via Topotactic Transformation, Toward Topological Superconductors

This paper demonstrates the successful growth of high-quality, air-stable superconducting PdTe thin films with bulk-like properties via molecular beam epitaxy using a topotactic transformation from a PdTe₂ buffer layer, establishing a promising platform for realizing topological superconductivity and Majorana zero modes.

Hee Taek Yi, Min Ge, Renjie Xie, Colby J. Stoddard, David H. Yi, Xiaoyu Yuan, Xiong Yao, Seongshik Oh2026-05-21🔬 cond-mat

Ultrafast excitation of Bloch plasmon polaritons in hyperbolic metamaterials with an extreme ultra-violet transient grating

This paper demonstrates that an extreme ultra-violet transient grating, formed by interfering free-electron laser pulses, can overcome momentum mismatch to enable the ultrafast excitation of Bloch plasmon polaritons in hyperbolic metamaterials, offering a dynamic alternative to permanent nanostructured gratings for controlling optical modes.

Tlek Tapani, Hannes Kempf, Matteo Pancaldi, Laura Foglia, Emanuele Pedersoli, Roberta Totani, Adriana Valerio, Riccardo Mincigrucci, Ivaylo Nikolov, Miltcho B. Danailov, Aitor De Andrés, Roman Krahne (…)2026-05-21🔬 physics.optics

TriForces: Augmenting Atomistic GNNs for Transferable Representations

TriForces is a model-agnostic, three-stream framework that combines self-supervised learning with separated composition and structure representations to significantly enhance the transferability and data efficiency of atomistic graph neural networks for machine learning interatomic potentials.

Ali Ramlaoui, Alexandre Duval, Hannah Bull, Victor Schmidt, Hugues Talbot, Fragkiskos D. Malliaros, Joseph Musielewicz2026-05-21🔬 cond-mat.mtrl-sci

Interacting donor-acceptor pairs as the origin of coupled spin-optical signals in hexagonal boron nitride

This paper utilizes first-principles calculations to demonstrate that the coupled spin-optical signals in hexagonal boron nitride originate from interacting donor-acceptor pairs rather than isolated defects, revealing how their separation and charge states govern key quantum properties and offering a unified framework for designing room-temperature quantum emitters.

Guanjian Hu, Jijun Huang, Bing Huang, Song Li2026-05-21🔬 cond-mat.mtrl-sci

Tuning the low-energy band structure in twisted bilayer WSe2

Using nano-ARPES, researchers demonstrate that while the momentum positioning of valence band maxima in twisted bilayer WSe2 remains fixed, the twist angle can be used to tune the energetic separation between hole bands at the K and Γ points by over 100 meV, offering a pathway to control band gaps and spin-dependent electron-phonon coupling in 2D devices.

T. -H. -Y. Vu, O. J. Clark, N. H. Jo, J. Blyth, Q. Li, C. Jozwiak, A. Bostwick, J. B. Muir, L. Jia, J. A. Davis, I. Di Bernardo, A. Grubisic Cabo, K. Xing, W. Zhao, S. H. Ryu, S. H. Lee, Z. Mao, K. Wa (…)2026-05-21🔬 cond-mat.mtrl-sci

Anisotropic Crystallization Kinetics and Interfacial Dynamics of Phase-Change Material Sb2_2S3_3 from Machine Learning Force Field Simulations

This study utilizes a machine learning force field to reveal that Sb2_2S3_3 exhibits anisotropic crystallization driven by its quasi-1D ribbon structure, with interface-controlled growth kinetics characterized by a significantly lower activation energy than diffusion, offering key insights for optimizing its performance in data storage and photonics applications.

Souvik Chakraborty, Wen-Qing Li, Yun Liu2026-05-21🔬 cond-mat.mtrl-sci