🔬 Category

cond-mat.mtrl-sci

4135 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

Griffiths-like phase, spin-phonon coupling, and exchange-bias in the disordered double perovskite GdSrCoMnO6_{6}

This study reveals that structural disorder in the double perovskite GdSrCoMnO6_6 drives complex magnetic behaviors, including a Griffiths-like phase, spin-phonon coupling, slow magnetic dynamics, and a significant low-temperature exchange-bias effect, all stemming from the competition between ferromagnetic and antiferromagnetic interactions caused by the random distribution of mixed-valence Co and Mn ions.

Gyanti Prakash Moharana, Diptikanta Swain, Hanuma Kumar Dara, Debendra Prasad Panda, S. N Sarangi2026-04-23
🔬 materials science

Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction

This study presents a validated machine learning framework, utilizing an ensemble CatBoost model trained on 410 samples, to rapidly predict and optimize the mechanical properties of resorbable magnesium alloys by elucidating the critical roles of thermomechanical processing and specific alloying elements like Zn, Mn, and Gd.

Vickey Nandal, Vít Beneš, Pavel Baláž, Jiří Ryjáček, Karel Tesař2026-04-23
🔬 materials science

Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning

This paper introduces a deep free energy learning framework that leverages the mathematical similarity between the self-consistent harmonic approximation free energy surface and potential energy surfaces to enable efficient, high-throughput crystal structure prediction incorporating finite-temperature and nuclear quantum effects, successfully identifying stable hydride structures in the La-Sc-H system with a million-fold reduction in computational cost compared to traditional methods.

Xiaoyang Wang, Yinan Wang, Wenbo Zhao, Hanyu Liu, Hao Xie, Lei Wang, Han Wang2026-04-23
🔬 materials science

Domain-Wall-Mediated Ultralow-Barrier Sliding and Pinning in Ferroelectric Moiré Superlattices Revealed by Machine Learning

This study employs machine-learning molecular dynamics to reveal that thermally driven interlayer sliding in ferroelectric MoS₂ moiré superlattices occurs via a domain-wall-mediated, ultralow-barrier collective reconstruction pathway rather than rigid translation, and that minimal sulfur vacancies can trigger a transition from long-range sliding to localized pinning.

Jia-Wen Li, Sheng Meng, Xinghua Shi, Jin Zhang, Wei-Hai Fang2026-04-23
🔬 materials science

LLM-guided phase diagram construction through high-throughput experimentation

This study demonstrates that large language models can effectively guide high-throughput experimentation to construct ternary phase diagrams, with a domain-specific LLM excelling at discovering complex interior phases and a general-purpose LLM efficiently identifying a broader range of phases through a textbook-like approach.

Ryo Tamura, Haruhiko Morito, Yuna Oikawa, Guillaume Deffrennes, Shoichi Matsuda, Naruki Yoshikawa, Tomoaki Takayama, Tai (…)2026-04-23
🔬 materials science

Multilayer Laue Lenses for Enhanced Spatial Resolution in Dark-Field X-ray Microscopy

This paper demonstrates that using crossed Multilayer Laue Lenses (MLLs) as objectives in Dark-Field X-ray Microscopy significantly enhances spatial resolution to 56 nm and increases numerical aperture by a factor of three compared to compound refractive lenses, thereby expanding the technique's capabilities for high-resolution bulk and near-surface imaging.

Steffen Staeck, Can Yildirim, Raquel Rodriguez-Lamas, Thomas Dufrane, Carsten Detlefs, Nis Gellert, Antonella Gayoso Pad (…)2026-04-23