比较水的机器学习潜力:基于内核的回归和Behler-Parrinello神经网络
Pablo Montero de Hijes1,2, Christoph Dellago1, Ryosuke Jinnouchi3
1University of Vienna, Faculty of Physics, Kolingasse 14, A-1090 Vienna, Austria.
The Journal of chemical physics
|March 20, 2024
概括
机器学习潜力 (MLP) 可以准确预测水的热力学特性. 对于可靠的模拟,高质量的数据比特定的装配方法更为重要.
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.
- 统计力学就是统计力学.
背景情况:
- 准确预测热力学属性对于理解水的行为至关重要.
- 机器学习潜力 (MLP) 为高效准确的模拟提供了一个有希望的途径.
研究的目的:
- 评估不同MLP在预测水的热力学特性方面的表现.
- 评估数据集大小和生成方法对MLP准确性的影响.
主要方法:
- 使用基于内核的回归和高维神经网络.
- MLP在使用精确方法和飞行学习生成的不同大小的数据集上接受了培训.
- 通过预测扩散常数,对相关函数和密度等差线来评估性能.
主要成果:
- 对于扩散常数和对相关函数,特别是在较大的数据集中,观察到很好的一致性.
- 预测密度等离子体显示了可接受的变化,考虑到近似密度函数理论中固有的错误.
- 在MLPs之间的微小差异并没有显著影响关键可观测的预测.
结论:
- 培训数据集的质量和规模比特定的MLP安装方法更为关键.
- 根平均平方误差有局限性;对于复杂的属性,建议使用多个MLP进行全面测试.
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