优化贝勒-帕里内洛神经网络架构的潜力
Lukáš Kývala1,2, Christoph Dellago1
1Faculty of Physics, University of Vienna, Kolingasse 14-16, 1090 Vienna, Austria.
The Journal of chemical physics
|September 1, 2023
概括
根据训练数据大小优化神经网络潜在架构可显著提高准确性. 太少的参数和太多的参数都会损害性能,两个隐藏层和无限制的激活功能被证明是最佳的.
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.
- 机器学习 机器学习
背景情况:
- 神经网络潜力 (NNP) 对于分子模拟至关重要.
- NNP架构通常是初始化后固定的,可能会限制准确性.
- 训练集大小对NNP架构和准确性的影响尚未完全理解.
研究的目的:
- 调查训练集大小对贝勒-帕里内洛神经网络潜在准确性的影响.
- 为了确定不同数据集大小的最佳神经网络架构.
- 分析建筑选择对NNP性能的影响.
主要方法:
- 在QM9和3BPA数据集上训练了Behler-Parrinello神经网络潜力.
- 不同的培训集大小和相应的网络架构.
- 分析了描述符复杂度,网络深度 (隐藏层数量) 和激活函数的影响.
主要成果:
- 调整网络架构以与训练集大小相匹配,大大提高了NNP的准确性.
- 不足和过多的装配参数数量都会对准确性产生负面影响.
- 两个隐藏层和无限制的激活功能为研究的NNP提供了最佳性能.
结论:
- 神经网络的潜在架构应该适应训练集大小,以获得最佳的准确性.
- 仔细考虑安装参数,网络深度和激活功能对于开发准确的NNP至关重要.
- 这项研究为设计更准确,更有效的神经网络潜能提供了指导方针.
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