合成计划,自动化和分析数据分析的工具
Miruna Cretu1, Marvin Alberts1, Anubhab Chakraborty1
1IBM Research Europe.
Chimia
|December 4, 2023
概括
机器学习帮助化学家进行计算机辅助合成设计,自动化和分析. 然而,有限的可访问工具阻碍了研究人员的广泛采用.
科学领域:
- 化学 化学 化学
- 计算机科学 计算机科学
- 数据分析数据分析数据分析.
背景情况:
- 机器学习 (ML) 为化学研究提供了强大的工具.
- 自动化和数据分析可以显著提高实验设计和效率.
研究的目的:
- 审查ML在合成设计,自动化和分析中的当前应用.
- 讨论这些计算方法的潜在好处和局限性.
- 确定这些工具融入研究工作流程中的障碍.
主要方法:
- 在计算机辅助合成,自动化和分析中对选定的作品的文献评论.
- 讨论各种ML方法及其适用于特定化学问题的讨论.
主要成果:
- 机器学习辅助工具可以自动执行常规任务,并提供数据驱动的洞察力.
- 这些工具使化学家能够更明智地进行实验设计.
- 目前的工具往往对非专家用户的进入障碍很高.
结论:
- 机器学习对通过自动化和增强分析来推进化学研究具有重大前景.
- 目前,由于可用的工具的可访问性和可用性,广泛采用目前受到限制.
- 开发用户友好的工具对于更广泛地融入研究实践至关重要.
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