🔬 Category

cond-mat.mtrl-sci

4090 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

Real-space identification of distinct magnetic configurations in a candidate d-wave altermagnet

By employing spin-polarized scanning tunneling microscopy and magnetic-field-dependent quasiparticle interference imaging, this study resolves the magnetic origin of momentum-dependent spin splitting in the candidate d-wave altermagnet KV2Se2O by identifying coexisting C-type and G-type magnetic configurations and establishing a direct link between real-space magnetic order and momentum-space electronic signatures.

Jin-Cheng Gu, Mingzhe Hu, Ziyin Song, Lihan Wang, Lihong Wang, Junming Zhang, Jiali Zhao, Hang Li, Shifeng Jin, Xin-Ding (…)2026-06-30
🔬 materials science

Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials

This paper introduces CliffordSTF, a novel interatomic potential that overcomes the directional inaccuracy of standard Cl(3,0)\mathrm{Cl}(3,0) geometric algebra models by coupling multivectors with symmetric-traceless tensor tracks, thereby achieving state-of-the-art performance in force direction and energy prediction across molecular and catalysis benchmarks without relying on Clebsch–Gordan coefficients or Wigner-DD matrices.

Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban2026-06-30
🔬 atomic physics

STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy

This paper introduces STEMGym, a benchmark demonstrating that in autonomous scanning transmission electron microscopy, optimizing the perception pipeline yields significantly greater dose efficiency than employing advanced adaptive navigation strategies, thereby reframing where machine learning efforts should be prioritized.

Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban2026-06-30
🔬 mesoscale physics

Length--Velocity Gauge Equivalence of Quantum Geometric Nonlinear Conductivity

This paper resolves the conceptual ambiguity regarding intrinsic second-order dc nonlinear conductivity by establishing a gauge-consistent density-matrix theory that proves the equivalence of length and velocity gauges, demonstrating that the adiabatic response is a purely Fermi-surface quantum geometric contribution determined by the band-normalized quantum metric, which vanishes in fully gapped insulators.

Shakeel Ahmad, Fei Xue2026-06-30
🔬 materials science

Field-induced topological Hall effect and butterfly-shaped magnetoresistance in the centrosymmetric antiferromagnet EuAuAs

This study reveals that the centrosymmetric antiferromagnet EuAuAs exhibits a field-induced topological Hall effect and butterfly-shaped magnetoresistance in its antiferromagnetic state, driven by finite scalar spin chirality and magnetic domain evolution.

Yu Zhang, Junfa Lin, Huan Wang, Kun Han, Yiting Wang, Xue Dong, Zhenfeng Guan, Shengdi Xi, Tian-Long Xia2026-06-30
🔬 materials science

Consistent transition model for Bi0.5Na0.5TiO3 from temperature-dependent structural and electrical properties

This study establishes a unified transition model for Bi0.5Na0.5TiO3 by integrating structural and electrical characterization techniques to resolve conflicting interpretations of its phase evolution, thereby providing a foundation for developing high-performance lead-free energy storage materials.

Thomas Fourgassie (Laboratoire GREMAN UMR7347, University of Tours, Tours, France, Université Paris-Saclay, CentraleSupé (…)2026-06-30
🔬 materials science

Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets

The paper introduces Atompack, an append-oriented storage format and distribution layer optimized for read-heavy atomistic machine learning training, which significantly outperforms existing baselines like ASE, LMDB, and HDF5 by delivering faster shuffled read throughput and producing substantially smaller dataset artifacts.

Ali Ramlaoui, Daniel T. Speckhard, Sagar Pal, Fragkiskos D. Malliaros, Alexandre Duval, Victor Schmidt2026-06-30