限制单层水/冰的温度-压力相位图在第一原理准确度与机器学习力场
Bo Lin1, Jian Jiang2,3, Xiao Cheng Zeng4,5
1Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices, Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China.
Nature communications
|July 11, 2023
概括
研究人员开发了一种机器学习的力场,以研究单层水/冰相在纳米限制中的行为. 这揭示了新的高密度冰和负压下低密度冰的稳定区域.
科学领域:
- 材料科学 材料科学 材料科学
- 物理化学 物理化学
- 纳米技术 纳米技术
背景情况:
- 了解纳米封闭水相态度对于各种科学和工程应用至关重要.
- 最薄的水膜的相位行为,特别是单层水,仍然不完全理解.
- 与散装水相比,纳米封闭显著改变了水的特性.
研究的目的:
- 开发一个高精度的机器学习力场 (MLFF) 来模拟单层水/冰.
- 为了确定由疏水壁所限制的单层水/冰的相位图.
- 探索二维水/冰的新型相位过渡和结构.
主要方法:
- 开发一个具有第一原则准确性的机器学习力场 (MLFF).
- 大规模的分子动力学 (MD) 模拟利用开发的MLFF.
- 封闭单层水/冰的相变和结构特征的分析.
主要成果:
- 观察到两种新型高密度冰的自发形成:齐格扎格的半平面冰 (ZZ-qBI) 和分支齐格扎格的半平面冰 (bZZ-qBI).
- 在ZZ-qBI和bZZ-qBI中确定了独特的结网络,与传统的双层冰相比,层间的结合较少.
- 在负压下 (<-0.3 GPa) 发现了低密度48分子单层冰 (LD-48MI) 的稳定区域.
- 模拟过渡到各种单层物体,包括六角形,五角形,方形,形 (ZZMI) 和六角形相.
结论:
- 该MLFF能够准确,大规模模拟单层水/冰阶段行为.
- 该研究扩展了已知的二维水/冰相图,揭示了新的高密度和低密度相.
- 结果提供了对纳米封闭水的基本见解,并指导未来对二维冰的实验研究.
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