通过机器学习探索超临界水中的酸反应网络 原子间潜力
Jae Hyun Ryu1, Soohee Kim1, Minwoo Kim1
1School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.
Journal of chemical information and modeling
|July 28, 2025
概括
机器学习潜能准确地模拟超临界的水氧化反应路径和产品,在废物处理优化方面表现优于传统的反应力场. 这种计算方法为复杂的分子机制提供了宝贵的见解.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 环境科学 环境科学
背景情况:
- 超临界氧化水 (SCWO) 是一个有前途的废物处理技术.
- 由于极端条件,了解SCWO复杂的分子反应机制是很困难的.
- 酸氧化在SCW中是一个关键的工业过程.
研究的目的:
- 将机器学习潜力 (NequIP) 和反应力场 (ReaxFF) 进行比较,用于在SCW中建模酸氧化.
- 评估NequIP和ReaxFF在预测反应路径,产品分布和机制方面的准确性.
- 评估这些计算方法对于优化工业氧化过程的适用性.
主要方法:
- 利用NequIP,一个机器学习潜力,和ReaxFF,一个反应力场,用于计算建模.
- 在不同氧化条件下 (O2和H2O2) 在超临界水中模拟酸氧化.
- 将模型预测与实验数据进行比较,以发现激活障碍,产品分布和反应途径.
主要成果:
- NequIP准确地复制了实验产品分布,并证实了激进反应机制.
- ReaxFF预测了接近实验值的激活障碍,但高估了中间稳定性,有利于不完全氧化.
- 两种模型都预测了过氧化的增强反应速率,但对特定步骤的影响不同.
结论:
- 像NequIP这样的机器学习潜力为模拟复杂的SCWO反应提供了准确性和效率的平衡.
- 与ReaxFF相比,NequIP提供了对SCWO机制和产品形成的卓越洞察力.
- 这些计算工具可以指导优化工业废物处理过程.
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