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Electron transfer rules of minerals under pressure informed by machine learning

Author

Listed:
  • Yanzhang Li

    (Peking University
    Peking University)

  • Hongyu Wang

    (Beihang University)

  • Yan Li

    (Peking University
    Peking University)

  • Huan Ye

    (Peking University
    Peking University)

  • Yanan Zhang

    (Beihang University)

  • Rongzhang Yin

    (Peking University
    Peking University)

  • Haoning Jia

    (Peking University
    Peking University)

  • Bingxu Hou

    (Peking University
    Peking University)

  • Changqiu Wang

    (Peking University
    Peking University)

  • Hongrui Ding

    (Peking University
    Peking University)

  • Xiangzhi Bai

    (Beihang University
    Beihang University
    Beihang University)

  • Anhuai Lu

    (Peking University
    Peking University)

Abstract

Electron transfer is the most elementary process in nature, but the existing electron transfer rules are seldom applied to high-pressure situations, such as in the deep Earth. Here we show a deep learning model to obtain the electronegativity of 96 elements under arbitrary pressure, and a regressed unified formula to quantify its relationship with pressure and electronic configuration. The relative work function of minerals is further predicted by electronegativity, presenting a decreasing trend with pressure because of pressure-induced electron delocalization. Using the work function as the case study of electronegativity, it reveals that the driving force behind directional electron transfer results from the enlarged work function difference between compounds with pressure. This well explains the deep high-conductivity anomalies, and helps discover the redox reactivity between widespread Fe(II)-bearing minerals and water during ongoing subduction. Our results give an insight into the fundamental physicochemical properties of elements and their compounds under pressure.

Suggested Citation

  • Yanzhang Li & Hongyu Wang & Yan Li & Huan Ye & Yanan Zhang & Rongzhang Yin & Haoning Jia & Bingxu Hou & Changqiu Wang & Hongrui Ding & Xiangzhi Bai & Anhuai Lu, 2023. "Electron transfer rules of minerals under pressure informed by machine learning," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37384-1
    DOI: 10.1038/s41467-023-37384-1
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    References listed on IDEAS

    as
    1. Arno Rohrbach & Max W. Schmidt, 2011. "Redox freezing and melting in the Earth’s deep mantle resulting from carbon–iron redox coupling," Nature, Nature, vol. 472(7342), pages 209-212, April.
    2. Jianfang Li & Zhaoyang Li & Xiangmei Liu & Changyi Li & Yufeng Zheng & Kelvin Wai Kwok Yeung & Zhenduo Cui & Yanqin Liang & Shengli Zhu & Wenbin Hu & Yajun Qi & Tianjin Zhang & Xianbao Wang & Shuilin , 2021. "Interfacial engineering of Bi2S3/Ti3C2Tx MXene based on work function for rapid photo-excited bacteria-killing," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
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    Cited by:

    1. Weihua Huang & Yan Yang & Yuan Li & Zheng Xu & Shuiyuan Yang & Shengbin Guo & Qunke Xia, 2024. "Inefficient nitrogen transport to the lower mantle by sediment subduction," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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