对计算化学家的自动化工作流管道的审查
Tong Wu1, Mingzi Sun1, Bolong Huang1
1Department of Chemistry, City University of Hong Kong, Hong Kong, 999077, China.
Small methods
|June 24, 2025
概括
本综述强调了用于高通量量子化学自动化工作流程的辅助工具. 这些工具对于机器学习模型的准确性至关重要,利用计算化学数据.
科学领域:
- 计算化学是一种计算化学.
- 人工智能的人工智能是人工智能.
- 机器学习 机器学习
背景情况:
- 计算化学为材料特性和实验结果提供了洞察力.
- 人工智能 (AI) 和机器学习 (ML) 在计算化学中越来越重要.
- ML模型的准确性在很大程度上取决于培训数据的质量和数量.
研究的目的:
- 审查自动化工作流程的辅助工具和代码.
- 在计算化学中解决机器学习的数据需求.
- 为了促进高通量量子化学方法.
主要方法:
- 专注于自动化工作流程和专用程序/代码.
- 探索用于批处理模型和数据的工具.
- 为AI/ML培训利用计算化学结果.
主要成果:
- 确定用于自动化工作流程的关键辅助工具.
- 展示了高通量量子化学的重要性.
- 突出数据质量在ML模型性能中的作用.
结论:
- 自动化工作流程对于高通量量子化学的高效性至关重要.
- 专门的工具增强了AI/ML在计算化学中的应用.
- 高质量的计算数据是推动AI/ML在现场发展的基础.
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