神经网络水模型基于MB-Pol多体潜力
Maria Carolina Muniz1, Roberto Car2, Athanassios Z Panagiotopoulos1
1Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States.
基于MB-pol的新型深度潜在神经网络 (DPMD) 模型准确模拟了水的特性. 仔细的训练集构建是模拟中准确和可转移的神经网络潜力的关键.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 物理化学 物理化学
背景情况:
- MB-pol多体潜能准确地模拟了水的特性,但在计算上很昂贵.
- 高计算成本限制了大规模模拟中准确潜力的应用.
研究的目的:
- 开发一个计算效率高的水深潜力神经网络 (DPMD) 模型,基于MB-pol潜力.
- 评估DPMD模型对各种水相和条件的准确性和可转移性.
主要方法:
- 训练了一个DPMD模型,使用MB-pol潜在的配置.
- 研究了训练集组合 (液体与集群配置) 对模型性能的影响.
- 评估模型预测液体,蒸汽-液体平衡和超冷水的性能的能力.
主要成果:
- 一个主要在液体配置上训练的DPMD模型准确地描述了散装液体相,但低估了蒸汽-液体共存密度.
- 将集群配置纳入训练集显著改善了对蒸汽共存密度的预测.
- 对于超冷却液态水密度,DPMD模型表现出很好的准确性,尽管这些条件不在训练集中.
结论:
- 神经网络模型,如DPMD,可以实现模拟水的准确性和可转移性.
- 培训套件的组成对于开发可靠和多功能神经网络潜力至关重要.
- 仔细构建代表性训练数据对于DPMD模型在材料模拟中的成功至关重要.
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