News from Science China Press

Researchers at Zhejiang University have developed AtomDiT, a machine learning framework that builds a digital twin of dislocations from a single high-resolution electron micrograph. The twin simultaneously reveals dislocation type and position, local stress, and chemical fields. Demonstrated across metals and alloys, including dual-phase systems and hydrogen-segregated grain boundaries, AtomDiT uncovers how stress and chemistry govern dislocation motion, offering a new route to understanding crystal deformation and damage.

In a paper published in SCIENCE CHINA Earth Sciences, a team of researchers led by Professor Jia Xin from the Institute of Environmental Archaeology at Nanjing Normal University reconstructed the environmental evolution and human settlement adaptation processes at the Xuecheng archaeological site in the Lower Reaches of the Yangtze River during the Mid to Late Holocene. By integrating multiple environmental proxies and high precision topographic data, the study revealed how hydrological and landscape changes shaped prehistoric settlement migration and spatial organization, providing new evidence for understanding human environment interactions during the formation of early Chinese civilization.

Circularly polarized light (CPL) carries spin angular momentum information and holds great promise for magnetic recording, spin optical communication, biological sensing and quantum computation. Conventional CPL detectors rely heavily on bulky and complex external optical components, which restrict device integration and miniaturization. Chiral organic semiconductors possessing an intrinsic capability for direct CPL discrimination, have emerged as promising candidates for CPL detectors. However, their development faces an intrinsic trade-off between charge carrier mobility and absorption dissymmetry factor.

Encouragingly, tremendous efforts have been devoted to resolving this contradiction, contributing to the development of a series of high-performance chiral organic semiconductors and thereby CPL detectors. Now, writing in Science Bulletin, researchers at the Institute of Chemistry, Chinese Academy of Sciences, systematically review representative progress of chiral organic semiconductors with a particular emphasis on the planarity-helicity dilemma and spectral tailoring. The review further highlights advances in CPL detectors and emerging applications, and discusses key challenges and future opportunities toward high-performance, miniaturized, and integrated CPL detection technologies.

Researchers have realized nonreciprocal dynamic degeneracy splitting in a gyromagnetic non-Hermitian photonic crystal. By engineering different lifetimes for opposite momentum channels at the same real frequency, the system turns loss into a directional selector, enabling momentum-selective bulk transmission and directional imaging.

Researchers developed physics-embedded inverse learning (PEIL), which uses a fixed, differentiable physical solver to turn signal-recovery error into supervision for parameter estimation without parameter labels. In high-mobility wireless tests, PEIL generalized to unseen channel profiles and speeds without retraining and matched a parameter-supervised baseline using one twentieth as many training samples. PEIL was also used to estimate coil sensitivity maps for parallel MRI.

The novel physics-constrained AI framework PHICS realizes a full closed loop spanning multiscale inverse design and experimental validation. Leveraging interface, phase, and carrier engineering, PHICS breaks long-standing material trade-offs to yield composite phase-change materials that achieve simultaneous enhancements in thermal conductivity and electrical insulation while maintaining high thermal storage density, paving new pathways for designing advanced electronic thermal management materials.

Embodied AI can act in the physical world, but the purpose guiding its actions often remains buried in prompts, rewards or predefined goals. In a Review published in National Science Review, Nanning Zheng proposes Intent-Driven Embodied Artificial Intelligence (IDEAI), a four-layer framework built around explicit, revisable and verifiable intent. Seven technical pillars map how that intent could shape knowledge, planning, safety and value alignment in real-world systems.