对基于深度学习的基本反应性质预测的不确定性资格
Yan Liu1,2, Yiming Mo1,3, Youwei Cheng1,2,4
1College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
Journal of chemical information and modeling
|October 23, 2024
概括
深度学习模型准确地预测化学反应特性,但往往缺乏不确定性量化. 这项研究将图形卷积神经网络与不确定性技术集成在一起,找到最适合可靠预测和不确定性估计的深层合并.
科学领域:
- 计算化学计算化学
- 化学动力学 化学动力学
- 机器学习 机器学习
背景情况:
- 深度学习 (DL) 显著推进了对基本反应的热力学和运动性质的预测.
- 然而,这些DL模型中预测不确定性的量化仍未得到充分研究,这限制了人们对其实际应用的信心.
研究的目的:
- 将图形卷积神经网络 (GCNN) 与不确定性量化技术集成.
- 评估不同不确定性预测方法 (深层集团,蒙特卡洛脱落,证据学习) 对化学反应性质的性能.
- 为了证明不确定性量化在改进动力模型中的实用性.
主要方法:
- 实现了GCNN结合深层组合,蒙特卡洛 (MC) 脱落和证据学习来预测不确定性.
- 利用蒙特卡罗树搜索 (MCTS) 来提取可解释的反应亚结构.
- 对DL构建的动力模型进行了不确定性引导的校准.
主要成果:
- 深层组合模型在各种数据集中展示了卓越的准确性和可靠的不确定性估计.
- 深层合奏模型有效地区分了认识论的不确定性和异构的不确定性.
- 与标准校准相比,以不确定性为指导的动力模型校准将路径识别提高了25%.
结论:
- 深层集体方法为基于DL的化学反应性质预测中不确定性量化提供了强大的方法.
- 可解释的AI技术,如MCTS,可以为DL预测及其不确定性提供化学洞察力.
- 不确定性量化对于提高DL生成的动力模型的可靠性和实用性至关重要.
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