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

Surface Modification for III-V Selective Area Molecular Beam Epitaxy of Non-Selective Mask Materials

This study demonstrates that depositing a sub-1 nm silicon dioxide capping layer enables selective area molecular beam epitaxy of III-V semiconductors on highly reactive or non-selective mask materials like TiO2TiO_2 and HfO2HfO_2, thereby overcoming the optical limitations of traditional masks without degrading their spectral performance.

Ashlee M. García, Byron D. Aguilar, William J. Doyle, Pernille Undrum Fathi, Federico Capasso, Daniel Wasserman, Seth R. (…)2026-06-02
🔬 materials science

Universal theory of domain-wall width in multi-sublattice Heisenberg magnets

This paper proposes a universal expression for the domain-wall width in multi-sublattice Heisenberg magnets by establishing an exact connection between the domain-wall profile and long-wavelength spin-wave dispersion, a framework that accurately predicts widths across various magnetic orders and lattice structures while providing a microscopic foundation for their temperature dependence.

José M. Lendínez, Marta Yanguas, Theodor Griepe, Michael Saur, Rubén M. Otxoa, Levente Rózsa, Unai Atxitia2026-06-02
🔬 materials science

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution

The paper introduces DPA4, a novel SE(3)-equivariant interatomic potential architecture featuring an EMFA SO(2)-equivariant convolution and compiler-friendly training optimizations that achieve state-of-the-art accuracy with significantly reduced parameter counts and training costs, establishing a new accuracy-cost Pareto frontier for large atomistic models.

Tiancheng Li, Wentao Li, Anyang Peng, Jianming Xue, Linfeng Zhang, Duo Zhang, Han Wang2026-06-02
🔬 materials science

Effect of annealing in the formation of well crystallized and textured SrFe12_{12}O19_{19} films grown by RF magnetron sputtering

This study demonstrates that ex-situ annealing transforms as-grown SrFe12_{12}O19_{19} films, which initially consist of nanocrystalline maghemite and amorphous strontium oxide, into well-crystallized, c-axis oriented strontium hexaferrite structures.

G. D. Soria, A. Serrano, J. E. Prieto, A. Quesada, G. Gorni, J. de la Figuera, J. F. Marco2026-06-01
🔬 materials science

Nanoscale Polar Landscapes in Quantum Paraelectric SrTiO3

Using cryogenic scanning transmission electron microscopy, researchers directly imaged the low-temperature structure of quantum paraelectric SrTiO3, revealing that its nanoscale polar domains initially self-organize into a periodic structure before fragmenting into small clusters as the material enters the quantum paraelectric regime below 40 K.

Yang Zhang, Suk Hyun Sung, Nishkarsh Agarwal, Maya Gates, Cong Li, Pu Yu, Robert Hovden, Ismail El Baggari2026-06-01
🔬 materials science

Performance Benchmarking of Tensor Trains for accelerated Quantum-Inspired Homogenization on TPU, GPU and CPU architectures

This paper benchmarks Tensor Train operations on CPUs, GPUs, and TPUs using JAX to adapt and accelerate a quantum-inspired SFFT-based homogenization algorithm, successfully enabling high-resolution multiscale simulations ranging from 300 million to 70 billion grid points that are infeasible with traditional GPU-based FFT methods.

Sascha H. Hauck, Matthias Kabel, Nicolas R. Gauger2026-06-01
🔬 materials science

Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters

By combining dispersion-corrected density functional theory with physics-informed machine learning, this study elucidates the sulfur adsorption and poisoning mechanisms across 30 transition metal 13-atom icosahedral clusters, identifying the Ti-Zr-Hf isoelectronic triad as a balanced group for designing sulfur-tolerant subnanometer catalysts.

Raiane Ferreira Monteiro, João Marcos T. Palheta, Tulio Gnoatto Grison, Octávio Rodrigues Filho, Renato Luis Tame Parrei (…)2026-06-01