六角冰密度依赖于核量子效应引起的原子间距离变化
Lucas T S de Miranda1, Márcio S Gomes-Filho2, Mariana Rossi3
1Institute of Theoretical Physics, São Paulo State University (UNESP), Campus São Paulo, São Paulo, Brazil.
The Journal of chemical physics
|September 4, 2025
概括
机器学习潜力显示大多数理论模型高估了六角冰密度. 量子核效应进一步增加了这种差异,加强了冰中的键.
科学领域:
- 凝聚物质物理学
- 计算化学
- 材料科学
背景情况:
- 六角冰 (冰Ih) 是最常见的冰,具有复杂的特性.
- 准确的冰密度和原子间相互作用的理论模型对于理解冰的行为至关重要.
- 机器学习潜力提供了一个有前途的方法,可以将初始准确性与经典分子动力学的可扩展性相结合.
研究的目的:
- 使用机器学习潜力研究冰的结构和振动特性.
- 评估不同交换相关函数对IH模拟的影响.
- 了解核量子效应对冰中的密度和结的作用.
主要方法:
- 开发和应用来自各种交换相关函数的机器学习潜力.
- 六角冰的模拟 (Ih) 侧重于结构和振动特性.
- 在模拟中包含核量子效应.
主要成果:
- 与实验数据相比,大多数测试的函数高估了冰的密度.
- 核的量子处理加剧了密度的高估,进一步偏离实验值.
- 核量子效应被发现在冰IH中强化键,与水集群或散水不同.
结论:
- 目前的机器学习潜力,特别是量子核处理,需要精确的冰密度预测.
- 了解原子间相互作用和核量子效应是改善冰IH理论模型的关键.
- 核量子效应对冰中的结合的明显影响需要进一步研究.
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