探测基于高吞吐量实验数据的机器学习模型,用于发现不对称的化催化剂
Adarsh V Kalikadien1, Cecile Valsecchi2, Robbert van Putten3
1Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology Van der Maasweg 9, 2629 HZ Delft The Netherlands e.a.pidko@tudelft.nl.
Chemical science
|August 30, 2024
概括
机器学习显示了加速在不对称化中的性联结体发现的前景. 然而,目前的模型在域外预测和反选择性方面遇到了困难,这凸显了对多样化的数据集和定制描述符的需求.
科学领域:
- 催化剂是一种催化剂.
- 机器学习 机器学习
- 计算化学计算化学
背景情况:
- 使用基于Rh的性催化剂进行异构选择性化是一个经过充分研究的领域.
- 对于新烯的配体选择仍然是一个经验,试错的过程.
- 加快催化剂的发现对于有效的化学合成至关重要.
研究的目的:
- 为了研究机器学习 (ML) 的应用,以识别高效的性连接体.
- 通过Rh催化不对称的烯酸的高通量实验来构建ML的大型数据集.
- 开发用于催化剂结构特征化的计算框架.
主要方法:
- 高通量实验以生成ML的数据集.
- 开发一个基于量子化学的特征化计算框架.
- 应用ML模型用于域内和域外选择性和反应性的预测.
- 对计算要求较高和较不密集的描述符的评估.
主要成果:
- 机器学习模型对域外预测的有效性有限,即使使用昂贵的描述符.
- 对转换的域内预测部分成功,表明需要对描述符进行成本效益分析.
- 预测enantioselectivity仍然具有挑战性,特别是在小数据集的情况下.
- 数据集的多样性和机械洞察力对于提高统计模型准确性至关重要.
结论:
- 机理学可以帮助在奇拉性连接体的发现,但目前存在广泛适用性的局限性.
- 计算描述符的成本效益需要仔细评估.
- 未来的努力应侧重于多样化的数据集和整合机理理解,以提高ML模型在催化中的性能.
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