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説明:

The molecular-to-atomic liquid-liquid transition (LLT) in high-pressure hydrogen is a fundamental
topic touching domains from planetary science to materials modeling. Yet, the nature of the
LLT is still under debate. To resolve it, numerical simulations must cover length and time scales
spanning several orders of magnitude. We overcome these size and time limitations by constructing a fast and accurate machine-learning interatomic potential (MLIP) built on the MACE neural network architecture. The MLIP is trained on Perdew-Burke-Ernzerhof (PBE) density functional calculations and uses a modified loss function correcting for an energy bias in the molecular phase. Classical and path-integral molecular dynamics driven by this MLIP show that the LLT is always supercritical above the melting temperature. The position of the corresponding Widom line agrees with previous ab initio PBE calculations, which in contrast predicted a first-order LLT. According to our calculations, the crossover line becomes a first-order transition only inside the molecular crystal region. These results call for a reconsideration of the LLT picture previously drawn.

権利情報:

• http://arxiv.org/licenses/nonexclusive-distrib/1.0/
  

キーワード: Machine learning potential, Hydrogen liquid-liquid transition

刊行年月日: 2025-09-24

出版者: American Physical Society (APS)

掲載誌:

• Physical Review B (ISSN: 1550235X) vol. 112 issue. 10 104208

研究助成金:

• Ministry of Education, Culture, Sports, Science and Technology JPMXS0320220025
• Japan Science and Technology Corporation JPMJBY24F3
• European High Performance Computing Joint Undertaking
• Horizon Foundation for New Jersey HORIZON-EUROHPC-JU-2022-INCO-04
• Horizon Foundation for New Jersey 952165

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI:

公開URL: https://doi.org/10.1103/pbrk-3zgd

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更新時刻: 2025-12-02 08:30:10 +0900

MDRでの公開時刻: 2025-12-02 08:23:29 +0900