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

Limited Diffusion of Silicon in GaN: A DFT Study Supported by Experimental Evidence

This study combines first-principles DFT calculations with ultra-high-pressure annealing experiments to demonstrate that silicon diffusion in gallium nitride is extremely limited due to prohibitively high activation barriers, thereby confirming the material's stability for precise doping in advanced electronic applications.

Karol Kawka, Pawel Kempisty, Akira Kusaba, Krzysztof Golyga, Karol Pozyczka, Michal Fijalkowski, Michal Bockowski2026-05-21
🔬 materials science

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy

This study demonstrates that machine-learned force fields trained on coupled-cluster data, enhanced by delta-learning and charge-aware approaches to address long-range effects and data limitations, achieve superior accuracy in predicting phonon dispersions and anharmonic vibrational properties for diamond and lithium hydride compared to traditional density functional theory.

Sita Schönbauer, Johanna P. Carbone, Fredrik V. Eriksson, Florian Libisch, Andreas Grüneis2026-05-21
🔬 mesoscale physics

A new skyrmion topological transition driven by higher-order exchange interactions in Janus MnSeTe

This study reveals a novel "ferric" topological transition in single-layer Janus MnSeTe driven by higher-order exchange interactions that specifically modify the Bloch point while leaving skyrmion stability largely governed by Dzyaloshinskii-Moriya interaction, establishing the material as a robust platform for 2D skyrmionics with exceptionally high energy barriers.

Megha Arya, Moritz A. Goerzen, Lionel Calmels, Rémi Arras, Soumyajyoti Haldar, Stefan Heinze, Dongzhe Li2026-05-21
🔬 materials science

Trigonal Distortion Driven Ground States in VX3 (X = Br and I)

This study utilizes high-resolution resonant inelastic x-ray scattering (RIXS) combined with ligand field multiplet calculations to comprehensively determine the ground state electronic structures of VBr3_3 and VI3_3, revealing distinct high-spin V3+^{3+} configurations driven by opposing trigonal distortions and increased covalency from bromine to iodine.

Chamini S. Pathiraja, Deniz Wong, Christian Schulz, Yi-De Chuang, Yu-Cheng Shao, Byron Freelon2026-05-21
🔬 materials science

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
🔬 materials science

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
🔬 condensed matter

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
🔬 optics

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 (…)2026-05-21