🔬 Category

cond-mat.mtrl-sci

4091 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

Capturing Nuclear Quantum Effects in Hydrogen Diffusion through MoS2 via Machine-Learning-Enhanced Path-Integral Simulations

This study employs machine-learning-enhanced path-integral simulations to demonstrate that nuclear quantum effects significantly lower free-energy barriers and induce a pronounced kinetic isotope effect for hydrogen diffusion in MoS2, while revealing that transport in twisted bilayer structures is strongly modulated by local stacking environments within moiré superlattices.

Ismail Eren, Ege Yigit Erbil, Maria-Judith Caisachana-Lozada, Hossein Mirhosseini, Thomas D. Kühne, Agnieszka B. Kuc2026-06-25
🔬 mesoscale physics

Residual orbital magnetization governs the anomalous Hall effect in altermagnets

This paper demonstrates that residual orbital magnetization, generated by the interplay of local crystal fields and spin-orbit coupling, intrinsically governs the anomalous Hall effect in altermagnets through the generalized Středa relation, challenging the view that their small remanent magnetization is irrelevant to transport phenomena.

Yufei Zhao, Yiyang Jiang, Kamal Das, Chao-Xing Liu, Binghai Yan2026-06-25
🔬 mesoscale physics

Measurements of absolute bandgap deformation-potentials of optically-bright bilayer WSe2_2

This study experimentally determines the absolute bandgap deformation potentials for the indirect and direct bandgaps of bilayer WSe2_2 using nanoparticle-induced strain and photoluminescence measurements, revealing that minimal localized strain can significantly enhance its optical brightness and enable direct bandgap conversion.

Indrajeet Dhananjay Prasad, Sumitra Shit, Yunus Waheed, Jithin Thoppil Surendran, Kenji Watanabe, Takashi Taniguchi, San (…)2026-06-24
🔬 mesoscale physics

Building unconventional magnetic phases on graphene by H atom manipulation: From altermagnets to Lieb ferrimagnets

This study demonstrates that single hydrogen atoms manipulated via scanning tunneling microscopy can be used to engineer and experimentally realize all fundamental non-relativistic magnetic phases, including altermagnetism and Lieb ferrimagnetism, within a single graphene platform.

B. Viña-Bausá, M. A. García-Blázquez, S. Chourasia, R. Carrasco, D. Expósito, I. Brihuega, J. J. Palacios2026-06-24
🔬 materials science

Influence of chemical ordering on magnetocrystalline anisotropy and magnetoelastic properties in Weyl magnetic semimetal Co2MnGa thin films

This study demonstrates that increasing chemical ordering in Co2MnGa thin films, particularly the formation of the L21 phase, significantly enhances both magnetocrystalline anisotropy and magnetoelastic properties, with the L21-ordered structure exhibiting giant magnetoelastic anisotropy distinct from non-Weyl semimetal counterparts.

O. Chumak, A. Nabiałek, L. T. Baczewski, T. Seki, J. Wang, K. Takanashi, H. Szymczak2026-06-24
🔬 mesoscale physics

Quantum Theory of Exciton Magnetic Moment: Interaction and Topological Effects

This paper presents a rigorous quantum theory of the exciton orbital magnetic moment that incorporates electron-hole interactions and quantum geometric effects, revealing three distinct contributions that resolve long-standing discrepancies between theoretical predictions and experimental observations in materials like biased bilayer graphene.

Gurjyot Sethi, Jiawei Ruan, Fang Zhang, Weichen Tang, Chen Hu, Mit Naik, Steven G. Louie2026-06-24
🔬 materials science

Active Learning Guided Computational Discovery of 2D Materials with Large Spin Hall Conductivity

This study employs an active learning framework guided by machine learning and density functional theory to efficiently discover 2D materials with exceptionally high spin Hall conductivity, identifying a top candidate nearly 23 times more effective than initial benchmarks while elucidating key atomic and electronic features that govern spin-charge interconversion.

Abhijeet J. Kale, Sanjeev S. Navaratna, Pratik Sahu, Henry Chan, B. R. K. Nanda, Rohit Batra2026-06-24
🔬 materials science

Precise Determination of the Long-Time Asymptotics of the Diffusion Spreadability of Two-Phase Media

This paper presents an improved algorithm for precisely determining the microstructural scaling exponent of two-phase media by incorporating higher-order correction terms and analyticity properties into the long-time asymptotics of diffusion spreadability, while also introducing a two-point Padé approximant to model the spreadability behavior across all time scales.

Shaobing Yuan, Salvatore Torquato2026-06-24
🔬 materials science

Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages

This paper demonstrates that machine-learning screens for sodium-cathode voltages, which rely on computationally derived references with systematic errors, failed pre-registered validation against experimental data due to a dominant 0.54 V bias in the reference values rather than the model itself, leading the authors to retire the screen and commit to a new calibration audit.

Krishna Teja Vepa2026-06-24✓ Author reviewed ⓘ
🔬 materials science

Interpretable Material Spatial Intelligence for Discovery of Governing Microstructural Features

This paper introduces Materials Spatial Intelligence (MSI), an interpretable machine learning framework that learns directly from multimodal spatial observations of material systems to predict macroscopic behavior, identify governing microstructural features, and enable mechanism-driven microstructure optimization.

Mathieu Calvat, Gregory Sparks, Dhruv Anjaria, Chris Bean, Haoren Wang, Paul Gradl, Timothy M. Smith, Allison M. Beese (…)2026-06-24