化学反应网络和机器学习的机会
Mingjian Wen1,2, Evan Walter Clark Spotte-Smith3,4, Samuel M Blau2
1Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA.
Nature computational science
|January 4, 2024
概括
化学反应网络 (CRN) 与机器学习 (ML) 结合,为理解复杂的化学系统提供了强大的工具. 本视角探讨了CRN的构建,分析和未来的ML集成,强调了关键的挑战和机遇.
科学领域:
- 化学 化学 化学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 化学反应网络 (CRN) 是模拟化学系统的基础.
- 越来越复杂的需求需要先进的分析和数据驱动的方法.
- 机器学习 (ML) 为CRN分析提供了新的能力.
研究的目的:
- 提供CRN构建和分析策略的全面概述.
- 讨论ML在CRN研究中的当前应用.
- 概述整合CRN和ML的未来方向.
主要方法:
- 审查现有的CRN构建和分析方法.
- 对CRN应用的当前ML技术的调查.
- 识别新兴的CRN-ML集成策略.
主要成果:
- 可用于CRN构建和分析的各种策略.
- 越来越多地应用ML技术来增强CRN研究.
- 对于未来的CRN-ML方法存在重大科学和技术挑战.
结论:
- CRNs和ML之间的协同作用对推进化学系统理解具有很大的前景.
- 需要进一步的研究来克服整合挑战.
- 未来的工作应该专注于开发强大的CRN-ML框架.
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