🔬 Category

cond-mat.mtrl-sci

4962 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

High-Throughput Bayesian Optimization of Cement-Salt Hydrates Composites for Seasonal Thermochemical Energy Storage

This paper demonstrates that a high-throughput Bayesian optimization framework effectively accelerates the discovery of cost-effective cement-salt hydrate composites for seasonal thermochemical energy storage, identifying Pareto-optimal formulations that significantly improve specific energy and cost-performance balance compared to previous cement-based materials.

Alessio Mondello, Giulio Barletta, Luca Lavagna, Matteo Fasano, Matteo Pavese, Eliodoro Chiavazzo2026-05-20
🔬 materials science

G0W0G_0W_0@HF and BSE methods in periodic systems from Hartree-Fock theory: gaussian orbital and density fitting approach

This paper presents a G0W0G_0W_0@HF and Bethe-Salpeter equation framework for periodic systems using Gaussian orbitals and density fitting, which corrects Hartree-Fock overestimations of band gaps and valence band widths in semiconductors and oxides by employing an exact RPA screening without plasmon pole approximations and a hybrid convergence strategy for virtual states.

Charles H. Patterson2026-05-20
🔬 materials science

Direct Simulation of LiNi0.8Mn0.1Co0.1O2 Transport Properties Using an Efficient and Accurate Machine Learning Potential

This study develops a data-efficient, accurate machine learning potential based on a fine-tuned MACE foundation model and active learning to enable large-scale molecular dynamics simulations that directly predict lithium self-diffusion coefficients in NMC811 cathode materials, overcoming the time and length scale limitations of traditional density functional theory.

Jian He, Constantijn H. J. A. van de Wetering, Rolande W. Nolsen, Nongnuch Artrith2026-05-20
🔬 applied physics

Building a Regional Data-Centric Materials Science Ecosystem for Processing-Rich Materials Innovation in the Great Plains

This paper proposes a regional data-centric ecosystem for the Great Plains to overcome barriers in materials innovation by integrating distributed experimental assets with FAIR metadata, uncertainty-aware modeling, and cross-trained workforces, using a high-purity germanium pilot to demonstrate how trustworthy data practices and interoperable infrastructure can drive processing-rich materials discovery.

D. -M. Mei, K. Acharya, C. M. Adhikari, M. Adhikari, S. Aryal, B. V. Benson, K. Bhatta, S. Bhattarai, N. Budhathoki, A. (…)2026-05-20
🔬 materials science

Partially reactive force field for the UiO-66 metal-organic framework

This paper introduces nb-UiO-FF, a novel partially reactive force field that accurately models the structural, mechanical, and defect properties of the UiO-66 metal-organic framework and enables molecular dynamics simulations of its solvothermal synthesis and self-assembly mechanisms.

Akanksha Nawani (Sorbonne Université, CNRS, Physicochimie des Electrolytes et Nanosystèmes Interfaciaux, PHENIX, Paris (…)2026-05-20
🔬 materials science

Adaptive Slater Koster Parameters: Crossing Oxidation States with Density Functional Tight Binding

This paper proposes an adaptive Density Functional Tight Binding (DFTB) method that dynamically adjusts Slater-Koster parameters based on local atomic environments and oxidation states using machine learning, achieving high accuracy in modeling electronic structures across diverse systems like oxidized nickel surfaces and lithium-intercalated graphite.

Yihua Song, Artem Samtsevych, Anton Beiersdorfer, Tobias Melson, Christoph Scheurer, Karsten Reuter, Chiara Panosetti2026-05-20
🔬 materials science

GenL: An extensible fitting program for Laue oscillations and whole pattern fitting

GenL is a flexible, extensible, and open-source MATLAB-based program that utilizes a genetic algorithm to simulate and fit X-ray reflectivity and diffraction data from epitaxial thin films, offering both source code and pre-compiled binary options for extracting structural parameters like strain profiles and crystal roughness.

Anna L. Ravensburg, Johan Bylin, Vassilios Kapaklis, Gunnar K. Pálsson2026-05-19