高维神经网络潜力用于准确预测状态方程:甲的案例研究
Mostafa Abedi1, Jörg Behler2,3, C Franklin Goldsmith1
1School of Engineering, Brown University, Providence, Rhode Island 02906, United States.
Journal of chemical theory and computation
|October 30, 2023
概括
机器学习分子动力学准确预测甲.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 化学物理 化学物理
背景情况:
- 机器学习的原子间潜力,特别是神经网络,对于分子动力学模拟至关重要.
- 在各种条件下准确预测材料特性对于科学进步至关重要.
研究的目的:
- 为了评估机器学习分子动力学的有效性,使用高维神经网络潜力 (HDNNPs) 来预测甲的状态特性方程.
- 为了比较两个不同的训练数据生成策略:批量表示与分子集群.
主要方法:
- 在机器学习分子动力学框架内利用高维神经网络潜力 (HDNNPs).
- 研究了两种数据生成方法:周期性批量细胞和分子.
- 在各种热力学条件 (液体,超临界,气相) 和液体-蒸汽共存曲线中预测的平衡物质密度.
主要成果:
- 与甲的参考相图取得了很好的一致性.
- 在所有研究阶段,平均预测误差低于2%.
- 验证了使用集群数据集训练的模型的准确性和可靠性.
结论:
- 机器学习分子动力学与HDNNP是预测甲状态方程的强有力的方法.
- 大量和基于集群的培训数据策略都产生了准确的结果,集群数据被证明是有效的.
- 这种方法为表征流体相和相变提供了可靠的工具.
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