Electron transfer rules of minerals under pressure informed by machine learning

Yanzhang Li, Hongyu Wang, Yan Li (), Huan Ye, Yanan Zhang, Rongzhang Yin, Haoning Jia, Bingxu Hou, Changqiu Wang, Hongrui Ding, Xiangzhi Bai () and Anhuai Lu ()
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Yanzhang Li: Peking University
Hongyu Wang: Beihang University
Yan Li: Peking University
Huan Ye: Peking University
Yanan Zhang: Beihang University
Rongzhang Yin: Peking University
Haoning Jia: Peking University
Bingxu Hou: Peking University
Changqiu Wang: Peking University
Hongrui Ding: Peking University
Xiangzhi Bai: Beihang University
Anhuai Lu: Peking University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37384-1

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