使用机器学习潜能对水杯进行计算研究.
Ryan J Szukalo1, Nicolas Giovambattista2, Pablo G Debenedetti3
1Department of Chemistry, Princeton University, Princeton, NJ 08544.
概括
机器学习模型准确地模拟了水.
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
背景情况:
- 水的异常特性是一个长期存在的科学难题.
- 液态-液态相变假设为理解这些异常提供了一个框架.
- 结晶化挑战已经将重点转移到无形冰状态.
研究的目的:
- 使用量子力学计算研究水的玻璃状现象学.
- 评估机器学习潜能在模拟无形冰的能力.
- 探索无形冰的行为和液体-液体过渡假设之间的关系.
主要方法:
- 利用了两种深潜力机器学习模型,在DFT和MP潜力上进行训练.
- 在水的玻璃状状态上进行了量子力学计算.
- 模拟液态水的同热火和同热压缩.
主要成果:
- 机器学习模型准确地捕捉了无形冰的结构和转变,尽管它们没有被明确训练.
- 观察到无形冰的连续性和在液体-液体临界压力附近的密度波动增加.
- 确定了低密度和高密度无形冰的两个不同的玻璃过渡温度分支,与实验数据和液体-液体过渡假设保持一致.
结论:
- 在平衡阶段训练的机器学习潜能可以有效地模拟非平衡的玻璃行为.
- 这些模型提供了一个强大的工具,以量子力学准确度研究水中的长时间,失衡的过程.
- 这项研究支持液态-液态相转换假设作为水异常的框架.
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