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Phase diagram and global structure search of bismuth using machine learning potential
Ziyang Yang1, Yijie Zhu1, Jiuyang Shi1
1National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
The Journal of Chemical Physics
|March 16, 2026
Summary
Researchers developed a machine learning potential for bismuth (Bi) to accurately model its high-pressure phases. This study reveals a new Bi phase, improving our understanding of its complex behavior under extreme conditions.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Computational Chemistry
Background:
- Bismuth (Bi) exhibits unique high-pressure phase behavior, crucial for technological applications and fundamental research.
- Accurate modeling of Bi's phase transitions under extreme conditions remains a significant challenge.
Purpose of the Study:
- To develop a highly accurate machine learning potential for modeling bismuth's phase behavior.
- To systematically explore the phase diagram of bismuth under high-pressure and high-temperature conditions.
Main Methods:
- Development of a neural equivariant potential machine learning potential for Bi.
- Integration with MAGUS crystal structure search and GPUMD molecular dynamics simulations.
- Enhanced sampling techniques for comprehensive phase exploration.
Main Results:
- Calculated solid-solid phase boundaries and solid-liquid coexistence line up to 4 GPa agree well with experimental data.
- Prediction of a new, dynamically stable phase of Bi (P42/mnm symmetry) around 2 GPa.
- This new phase is energetically competitive with the known C2/m phase near the melting line.
Conclusions:
- The developed machine learning potential offers near first-principles accuracy for modeling Bi.
- The study enhances understanding of bismuth's high-pressure behavior and predicts novel phase transitions.
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