数字准确性问题:机器学习潜在能量表面的应用
1Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland.
The journal of physical chemistry letters
|March 20, 2024
概括
数字精度对于神经网络的潜在能量表面 (PES) 是至关重要的. 对于精确的高阶导数,需要双重精度,而单一精度则足以进行分子动力学模拟.
科学领域:
- 计算化学计算化学
- 机器学习在科学中的应用
- 量子力学就是量子力学.
背景情况:
- 基于神经网络的潜在能量表面 (PES) 越来越多地用于计算化学.
- 计算的数值精度可能会影响这些PES的准确性.
- 不同的实验可观测物可能对PES衍生品的准确性有不同的敏感性.
研究的目的:
- 调查数字精度对培训的影响,并评估基于神经网络的PES.
- 为了确定不同类型的计算和可观测的所需精度.
- 评估单精度算法与双精度算法的适合性,用于 PES 的开发.
主要方法:
- 基于神经网络的 PES 训练,使用单精度和双精度算法.
- 通过检查三级和四级衍生品来评估 PES 的准确性.
- 计算对基准分子 (H2CO,甲) 的无和频率和道分裂.
- 评估 PES 适合用于需要一阶衍生物的分子动力学模拟.
主要成果:
- 单精度算法导致粗略的PES和不准确的更高阶衍生值,不适合需要它们的可观测值.
- 双精度算法产生了平滑的PES,其数值稳定的高阶衍生值.
- 双精度可以准确预测无声频率和道分裂.
- 单一精度足以用于仅需要第一阶衍生物的分子动力学模拟.
结论:
- 双精度算法对于开发基于神经网络的精确PES是必不可少的,当需要更高阶衍生品时.
- 仔细考虑数值精度对于可靠的计算化学预测至关重要.
- 数字精度的选择应以预期应用和可观测的具体要求为指导.
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