2794 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.
This paper reports the experimental observation of acoustic magneto-chiral anisotropy in -quartz using a high-resolution ultrasound spectrometer, supported by a macroscopic analytical model that explains the effect's magnitude and frequency dependence.
This paper introduces Equitrain, a LoRA-based fine-tuning framework that significantly enhances the accuracy of machine-learning interatomic potentials for predicting phonon and thermal properties across diverse materials using minimal additional training data, outperforming both pretrained and scratch-trained models.
This paper proposes Adversarial Distribution Alignment (ADA), a domain-agnostic framework that bridges the simulation-to-experiment gap by pre-training a generative model on fully observed simulation data and then aligning it with partial experimental observations to recover the target observable distribution.
This paper presents a theoretical framework and experimental validation demonstrating that atomic-level interfacial broadening in (SiGe)m/(Si)m superlattices induces localized energy states that create new optical absorption paths between 2 and 2.5 eV, enabling a non-destructive method to probe interfacial atomic structure.
This paper introduces a novel framework for time-dependent global sensitivity analysis applied to the Doyle-Fuller-Newman battery model, enabling the identification of insensitive parameters and the assessment of model error when those parameters are arbitrarily set, thereby facilitating more efficient simulative research on time-dependent outputs like voltage responses.
This study employs a multi-objective Bayesian optimization framework with an ensemble surrogate model and efficient sampling strategy to successfully identify Pareto-optimal high-entropy alloy compositions that simultaneously achieve high mechanical hardness and soft magnetic properties, overcoming the inherent trade-off between these characteristics.
This study benchmarks five foundational machine learned interatomic potentials against DFT-NEB calculations to evaluate their accuracy in predicting ionic migration barriers, revealing that models like MACE-MP-0 and Orb-v3 excel in barrier prediction and high-throughput screening despite a lack of correlation with local geometry accuracy.
This study utilizes a machine learning potential to demonstrate that uniaxial compressive stress significantly influences polarization switching and domain wall evolution in tetragonal BaTiO3, revealing a critical threshold of approximately 120 MPa for 90-degree switching, stress-induced reductions in remnant polarization and coercive field, and the emergence of double hysteresis loops at 80 MPa.
This study demonstrates that controlling sulfur stoichiometry and growth kinetics during molecular beam epitaxy on silicon substrates enables the creation of defect-engineered Mo/MoS heterostructures with residual metallic Mo and sulfur vacancies, which significantly enhance hydrogen evolution reaction performance by activating inert basal planes and improving charge transfer compared to stoichiometric films.
This paper presents a robust convolutional neural network trained on realistic micromagnetic simulations that accurately and reliably estimates interfacial Dzyaloshinskii-Moriya interaction strength from magnetic bubble domain textures, offering a fast and quantitative alternative to inconsistent experimental methods.