2792 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 introduces a symmetry-based framework called "order-(dis)order family trees" to evaluate the true novelty of predicted crystal structures by identifying their relationships to known disordered parent phases, revealing that many seemingly novel ordered compounds are actually derived from existing disordered materials and highlighting the importance of this distinction for improving data-driven materials discovery.
The paper introduces FINDSPINGROUP, an online computational platform that unifies spin space group and magnetic space group frameworks to automate symmetry analysis, classify magnetic phases, and facilitate the high-throughput discovery of unconventional magnets for next-generation spintronics.
This paper presents the monolithic back-end-of-the-line integration of compact, energy-efficient VO-based spiking neurons on CMOS-compatible platforms, demonstrating gate-tunable oscillations, low-power operation, and tunable coupling that pave the way for dense neuromorphic hardware.
This paper presents a design optimization scheme for magnetic tunnel junction-based sensors that balances flux concentrator gain and magnetic noise through finite element simulations and analytical modeling, ultimately achieving a three-orders-of-magnitude performance improvement over single-junction designs.
This paper demonstrates that existing methods fail to quantify rotational motion in complex molecular systems like supercooled liquids and introduces a new empirical method that accurately captures the full spectrum of rotational dynamics from diffusive fluids to arrested solids, thereby resolving inconsistencies in the literature.
This review article examines the fundamental mechanisms, experimental advances, and theoretical insights regarding the formation and manipulation of chiral spin textures in van der Waals heterostructures, while outlining future challenges for developing robust, room-temperature spintronic devices.
This paper proposes a self-consistent interpolation scheme based on the matrix logarithm that significantly improves the accuracy of Berry connection evaluations and optical conductivity calculations by explicitly accounting for the matrix structure of overlap matrices and quantifying the impact of basis set incompleteness.
This study demonstrates that substituting Fe with Mn in PrFeAs(O,F) acts as a potent magnetic impurity that suppresses superconductivity and induces insulating-like behavior, while also revealing the enhanced robustness of superconductivity in Pr-based systems compared to other rare-earth variants.
This paper demonstrates a scalable method for intercalating nickel beneath epitaxial graphene on SiC(0001) using colloidal nanoparticle deposition and thermal annealing, resulting in a stable 2D heterostructure with robust interfacial magnetism that is promising for next-generation spintronic applications.
This paper compares two Convolutional Recurrent Neural Network architectures for simulating crystal growth under variable supersaturation, demonstrating that explicitly conditioning the network on the supersaturation parameter yields superior high-fidelity predictions compared to implicit inference via input mini-sequences, while both models exhibit strong scalability to larger domains and longer time sequences.