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.

Atomistic mechanism and interface-structure-energetics of van der Waals epitaxy demonstrated by layered alpha-MoO3 growth on mica

This study elucidates the atomistic mechanism of van der Waals epitaxy for α\alpha-MoO3_3 on mica by combining experimental characterization of stress-free, multi-oriented growth with ab initio calculations that identify specific Mo-K proximity as the key driver for interface energetics, thereby establishing a predictive framework for designing stress-free epitaxial films of layered materials.

Faezeh A. F. Lahiji, Davide G. Sangiovanni, Biplab Paul, Justinas Palisaitis, Per O. A. Persson, Arnaud le Febvrier, Ganpati Ramanath, Per Eklund2026-04-07🔬 cond-mat.mtrl-sci

Electron-electrolyte coupling in AC transport through nanofluidic channels

This paper investigates AC-driven transport in nanofluidic channels to reveal how capacitive coupling between channel wall electrons and electrolyte ions creates distinct frequency-dependent signatures, modifies electro-osmotic flows, and establishes a comprehensive transport matrix linking ionic, electronic, and hydrodynamic phenomena.

Baptiste Coquinot, Mathieu Lizée, Lydéric Bocquet, Nikita Kavokine2026-04-07🔬 cond-mat.mes-hall

Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique

This paper introduces a Lagrangian-based technique that replaces the computationally expensive requirement of 6N6N DFT calculations with a single coupled-perturbed Kohn-Sham calculation to efficiently generate unbiased atomic forces in ab initio Variational Monte Carlo, thereby improving their consistency with potential energy surfaces and accuracy relative to CCSD(T) benchmarks.

Kousuke Nakano, Stefano Battaglia, Jürg Hutter2026-04-07🔬 cond-mat.mtrl-sci

Polymer-Iron Oxide Hybrid Films for Controlling Electrokinetic Properties

This paper demonstrates a simple liquid-phase infiltration method to synthesize polymer-iron oxide hybrid films that effectively transfer the electrokinetic properties of iron oxide to polymer surfaces, offering a scalable strategy for controlling interfacial charge in applications such as water purification and energy conversion.

Austin Dick, Xiao Tong, Kim Kisslinger, Carlos E. Colosqui, Gregory Doerk2026-04-07🔬 cond-mat.mtrl-sci

Inverse magnetic melting effect in vdW-like Kondo lattice CeSn0.75_{0.75}Sb2_2

This study reports the synthesis of single-crystalline quasi-two-dimensional Kondo lattice CeSn0.75_{0.75}Sb2_2 and demonstrates a rare inverse magnetic melting effect where low in-plane magnetic fields restore translational and rotational symmetries by transforming a fragile antiferromagnetic order and cluster glass ground state into a polarized paramagnetic phase.

Hai Zeng, Yiwei Chen, Zhuo Wang, Shuo Zou, Kangjian Luo, Yang Yuan, Meng Zhang, Yongkang Luo2026-04-07🔬 cond-mat.mtrl-sci

Scaling atom-by-atom inverse design with nano-topology optimization and diffusion models

This paper introduces an atom-by-atom inverse design framework that integrates Nano-Topology Optimization with conditional diffusion models to overcome continuum limitations by explicitly accounting for crystal symmetry and surface physics, thereby enabling the discovery of high-performance metallic nanostructures like aluminum nanocantilevers and nanopillars.

Chun-Teh Chen, Denvid Lau2026-04-07🔬 physics.app-ph

Generative Chemical Language Models for Energetic Materials Discovery

This paper introduces a transfer-learning framework utilizing generative molecular language models, pretrained on extensive chemical data and fine-tuned with curated energetic materials datasets, to overcome data scarcity and accelerate the discovery of next-generation energetic materials through fragment-based encodings.

Andrew Salij, R. Seaton Ullberg, Megan C. Davis, Marc J. Cawkwell, Christopher J. Snyder, Cristina Garcia Cardona, Ivana Matanovic, Wilton J. M. Kort-Kamp2026-04-07🔬 physics