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Showing 1–5 of 5 results
Advanced filters: Author: Zhonghang Chen Clear advanced filters

  • Solving the many-body electronic structure of real solids is a grand challenge in condensed matter physics and materials science. Here authors present a machine learning ab initio architecture for real solids, which combines molecular neural network wavefunction ansatz and periodic features, providing accurate solutions for a range of solids.

    • Xiang Li
    • Zhe Li
    • Ji Chen
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-9

  • An accurate ab initio calculation of molecules is fundamental to chemical and physical sciences. Here, the authors integrate a neural-network wavefunction into the fixed-node diffusion Monte Carlo, resulting in accurate calculations of a diverse range of systems, offering insights into complex many-body electronic wave functions.

    • Weiluo Ren
    • Weizhong Fu
    • Ji Chen
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-12

  • Detection of trimethylamine N-oxide (TMAO) can allow for early intervention of cardiovascular disease, but is challenging to achieve using conventional materials and instruments owing to it being spectroscopically silent in the UV-visible region. Here, a series of bilanthanide metalorganic frameworks functionalised with a borono group are shown to detect TMAO with high sensitivity and selectivity by exploiting the inverse emission intensity changes of the two lanthanide centres.

    • Hui Min
    • Zhonghang Chen
    • Peng Cheng
    ResearchOpen Access
    Communications Chemistry
    Volume: 5, P: 1-8