不确定性通过贝叶斯科学机器学习量化化学反应系统的发现
Emily Nieves1,2, Raj Dandekar1, Chris Rackauckas1
1Department of Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.
Frontiers in systems biology
|August 14, 2025
概括
这项研究引入了一种概率方法,使用贝叶斯推理和神经普通微分方程 (ODEs) 来发现化学反应途径. 该方法为学习反应网络提供不确定性估计,提高了准确性和稳定性.
科学领域:
- 计算化学的计算化学
- 在化学科学中的机器学习
- 系统生物学 系统生物学
背景情况:
- 化学反应神经网络 (CRNNs) 从时间解析的度数据确定性地识别反应途径.
- 由于具有物理意义的权重和偏差,CRNN作为化学反应网络的可解释数字双胞胎.
研究的目的:
- 开发一种用于发现化学反应路径的概率方法,使用贝叶斯推理与神经普通微分方程 (ODEs) 结合.
- 估计与学习化学反应网络相关的不确定性.
- 为贝叶斯推理对复杂的化学和生物反应系统提供一个强大的框架.
主要方法:
- 神经ODEs与预先条件的随机梯度Langevin下降 (pSGLD) 的整合贝叶斯框架.
- 算法对神经网络重量进行后置采样,用于概率性路径发现.
- 对PSGLD与标准SGLD进行后置估计效率和准确性的比较.
- 纳入科学知识以提高抽象精度.
主要成果:
- 在各种系统中成功恢复化学反应路径.
- 准确估计预测反应路径中的不确定性.
- 证明pSGLD提供比SGLD更有效和更准确的后部估计.
- 与纯数据驱动方法相比,当科学知识被嵌入时,提取准确度得到了提高.
结论:
- 建议使用神经ODEs和psGLD的贝叶斯框架,可以通过不确定性量化对化学反应途径的概率发现.
- 该方法为分析复杂的化学和生物系统提供了强大的和自主性的方法.
- 嵌入科学知识可以提高模型的预测能力和通用性.
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