机器学习粗粒水模型中的液体-液体过渡和冰结晶
Debdas Dhabal1, Rajat Kumar1, Valeria Molinero1
1Department of Chemistry, The University of Utah, Salt Lake City, UT 84112-0850.
概括
一个新的机器学习水模型 (ML-BOP) 揭示了超冷水中的液体-液体过渡 (LLT),这对于理解冰的形成至关重要. 这个模型有效地模拟了水.
科学领域:
- 物理化学 物理化学
- 计算材料科学科学 计算材料科学
- 统计力学 统计力学
背景情况:
- 实验证据表明,在高压超冷水中存在液体-液体过渡 (LLT).
- 快速结晶阻碍了对LLT线的实验识别.
- 全原子 (AA) 模型显示LLT,但在计算上昂贵;粗粒 (CG) 模型是高效的,但缺乏LLT.
研究的目的:
- 为了证明一个粗粒度 (CG) 机器学习的水模型 (ML-BOP) 呈现出液体-液体过渡 (LLT).
- 研究LLT与超冷水中的冰结晶之间的关系.
- 验证ML-BOP能够复制水的液相行为实验观测的能力.
主要方法:
- 使用粗粒度机器学习的水模型,ML-BOP.
- 在高压下对超冷水进行冷却模拟.
- 分析了LLT线,临界点和冰结晶行为.
主要成果:
- ML-BOP表现出一个在临界点结束的LLT (Pc = 170 ± 10 MPa,Tc = 181 ± 3 K).
- ML-BOP中的LLT线与TIP4P/2005模型的线路非常相似.
- 冰结晶在LLT和其超临界延续处是最快的,这表明有机械联系.
结论:
- ML-BOP成功地在超冷水中模拟了LLT,弥合了AA和CG模型之间的差距.
- 该研究支持水的结构转变和冰的形成之间的机制关系.
- ML-BOP复制了低密度液体 (LDL) 和冰形成之间的实验竞争,即使结晶之前域粗化.
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