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

General Learning of the Electric Response of Inorganic Materials

The paper introduces \texttt{MACE-Field}, an O(3)O(3)-equivariant interatomic potential that integrates a uniform electric field into the MACE backbone to accurately predict diverse inorganic materials' dielectric, ferroelectric, and spectroscopic properties through exact differentiation of a learned electric enthalpy functional.

Bradley A. A. Martin, Alex M. Ganose, Venkat Kapil, Tingwei Li, Keith T. Butler2026-06-09
🔬 materials science

Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?

This study utilizes machine learning and diffusion-based structure prediction to reveal that amorphous lithium difluorophosphate (\ce{LiPO2F2}), a key solid-electrolyte interphase component, exhibits high ionic conductivity due to structural disorder and abundant interstitial defects, suggesting that amorphous mixed-anion phases are the primary fast-ion pathways in Li-ion batteries.

Peichen Zhong, Kristin A. Persson2026-06-09
🔬 materials science

Data-model Coevolution as the Architectural Principle for AI-Native Materials Databases

This paper proposes and validates "data-model coevolution" as a foundational architectural principle for AI-native materials databases, demonstrating through a Li-P-S ternary prototype that endogenous generation-evaluation-refinement cycles can autonomously discover novel stable phases and achieve high-precision predictive modeling with minimal first-principles cost.

Fengyu Xie, Ruoyu Wang, Taoyuze Lv, Yuxiang Gao, Hongyu Wu, Zhicheng Zhong2026-06-09
🔬 materials science

Momentum-Resolved Electronic Structure and Orbital Hybridization in the Layered Antiferromagnet CrPS4_4

This study combines momentum-resolved photoemission spectroscopy and DFT+U calculations to experimentally characterize the electronic band structure of the layered antiferromagnet CrPS4_4, revealing a ligand-to-metal charge-transfer gap and distinct orbital hybridization patterns that govern its magnetic and optical properties.

Lasse Sternemann, David Maximilian Janas, Eshan Banerjee, Richard Leven, Jonah Elias Nitschke, Marco Marino, Leon Becker (…)2026-06-09
🔬 materials science

A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

This paper introduces a large-scale, aligned Nanocrystal Synthesis-Property database constructed using the LLM-enhanced NanoExtractor tool, which enables the generative inverse design of viable nanocrystal synthesis routes through the NanoDesigner model, successfully validated by experimental confirmation of both established and novel nanocrystal formulations.

Kai Gu, Yingping Liang, Senliang Peng, Aotian Guo, Haizheng Zhong, Ying Fu2026-06-09
🔬 materials science

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

The paper introduces MOF-LLM, a novel framework that enhances the spatial reasoning capabilities of a Qwen-3 8B language model through spatial-aware continual pre-training, supervised fine-tuning, and reinforcement learning to achieve state-of-the-art, high-efficiency block-level 3D structure prediction for Metal-Organic Frameworks.

Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang2026-06-09
🔬 materials science

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

MatMind is a unified generative foundation model for crystal materials science that integrates structure-activity knowledge and physics-informed feedback to surpass specialized narrow architectures in both property prediction and crystal generation tasks.

Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youw (…)2026-06-09
🔬 optics

An ultra-wide-bandgap semiconductor photodetector for linear measurement of bright sub-bandgap light

This paper demonstrates that sub-bandgap AlN photodetectors, engineered with specific dopant designs and contact structures to create a narrow space charge region, achieve non-saturating, linear responses to ultra-bright blue light and elevated temperatures by leveraging deep-level defect-mediated photoresponse, thereby enabling reliable sensing in extreme industrial and aerospace environments.

Jiahao Dong, Zhenjing Liu, Rafael Jaramillo2026-06-09