Machine Learning simulations reveal oxygen’s phase diagram and thermal properties at conditions relevant to white dwarfs

Yunlong Wang, Jiuyang Shi, Zhixin Liang, Tianheng Huang, Junjie Wang, Chi Ding (), Chris J. Pickard, Hui-Tian Wang, Dingyu Xing, Dongdong Ni () and Jian Sun ()
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Yunlong Wang: Nanjing University
Jiuyang Shi: Nanjing University
Zhixin Liang: Nanjing University
Tianheng Huang: Nanjing University
Junjie Wang: Nanjing University
Chi Ding: Nanjing University
Chris J. Pickard: 27 Charles Babbage Road
Hui-Tian Wang: Nanjing University
Dingyu Xing: Nanjing University
Dongdong Ni: Nanjing University
Jian Sun: Nanjing University

Nature Communications, 2025, vol. 16, issue 1, 1-7

Abstract: Abstract Current studies show that oxygen does not aggregate into a polymeric phase even under pressures up to 10 TPa. To address the critical knowledge gap in understanding dense oxygen, here we show the complete polymerization process of oxygen, by using structure prediction methods. We determine the crystal structures of oxygen up to 1 PPa (1000 TPa), identifying a novel two-dimensionally bonded body-centered tetragonal (bct) phase and a fully polymerized hexagonal close-packed (hcp) phase. Electronic structure analysis reveals significant bond softening in the bct phase with increasing pressure, which may affect the dynamic behavior under finite temperatures. So, we employ the machine learning potential molecular dynamics and the two-phase method to construct the melting curve of oxygen up to 200 TPa (200 TPa, 23,740 K) and identify abnormal melting behavior beyond 100 TPa. We find oxygen exhibits higher thermal conductivity and lower isochoric heat capacity than helium at identical pressures. These results indicate that oxygen-rich envelopes may accelerate the cooling process of white dwarfs.

Date: 2025
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DOI: 10.1038/s41467-025-61390-0

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