Researchers developed an AI-assisted computational pipeline that combines symmetry-guided screening, machine learning, and density functional theory to search for altermagnetic metal-organic frameworks. The approach also identified molecular features associated with altermagnetism and spin splitting, providing design rules for selecting future candidates for computational and experimental study.
As transistors approach atomic dimensions, the interface between different materials grows in importance. Researchers from?National Yang Ming Chiao Tung University (NYCU), TSMC Corporate Research, and colleagues from National Taiwan University, Academia Sinica, and the National Center for Instrumentation Research have devised an interface engineering approach to address a major challenge in two-dimensional (2D) transistors.
Flexible NIR and SWIR image sensors combine infrared spectral sensing with conformability, offering new possibilities for wearable healthcare, agriculture, inspection, security, robotics, and human-computer interaction. The review highlights advances in organic semiconductors, quantum dots, perovskites, 2D materials, device architectures, and computational imaging while identifying stability, readout integration, and standardized performance measurement as key barriers to practical deployment.
Ultra-thin two-dimensional (2D) materials are only one atom thick. They can act as powerful filters at the tiniest scales, even approaching sub-atomic dimensions. In this work, scientists used machine learning to develop a scalable method to grow high-quality (almost defect-free), atom-thin layers of boron and nitrogen.
Researchers developed a microscopic theory in which intra-valley, finite-momentum pair-density waves generate the Kekulé patterns observed in magic-angle twisted bilayer graphene. The model links spin-triplet pairing, electronic nematicity, and Bogoliubov Fermi surfaces to key experimental signatures of unconventional superconductivity.
Chiral 2D metal halide perovskites (MHPs) are among the most promising materials for future technologies that exploit the spin of electrons in spin-based optoelectronics or spintronics, but getting them to perform consistently has proven difficult.
Researchers used pulsed sub-atomic layer deposition to control both composition and metal-atom distribution in Mo-W-S2 monolayers across the full MoS2-to-WS2 range. The study shows how engineered line structures and elongated islands can shift excitonic emission, pointing toward future quantum-dot and quantum-wire-like 2D materials.
Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory are helping show what it means to design a material almost atom-by-atom.
Qumus is a physically embodied, multi-agent AI system that autonomously plans, executes, analyzes, and refines quantum-materials experiments inside a robotic minilab.
Amino resin-derived carbon fiber paper shows enhanced strength and conductivity at lower temperatures, offering a breakthrough for electrochemical systems.
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