Predicting ruthenium catalysed hydrogenation of esters using machine learning
Predicting ruthenium catalysed hydrogenation of esters using machine learning
复制标题
使用机器学习预测钌催化的酯氢化
DOI：
10.1039/d3dd00029j
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Mishra C
中科院分区：
文献类型：
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作者：
Mishra C
酯的催化氢化是生产精细化学品和药物的可持续方法。然而，催化剂的效率和成本往往是此类技术商业化的瓶颈。催化剂发现的传统方法基于经验主义，这使得发现过程既耗时又昂贵。迫切需要开发有效的方法来发现加氢反应的有效催化剂。在这项工作中，我们探索了机器学习方法，使用各种机器学习架构（NN、GP、决策树、随机森林、KNN 和线性回归）来预测酯催化氢化的结果。我们的优化模型可以以合理的误差预测反应产率，例如，使用 GP 对未见数据进行均方根误差 (RMSE) 为 11.76%，并表明选择性地使用某些化学描述符（例如电子参数）可以产生更准确的模型。此外，还进行了催化剂和反应条件（例如温度和压力）的预测以及通过进行加氢反应进行验证以改善数据集中描述的不良产率的研究。
Catalytic hydrogenation of esters is a sustainable approach for the production of fine chemicals, and pharmaceutical drugs. However, the efficiency and cost of catalysts are often bottlenecks in the commercialization of such technologies. The conventional approach to catalyst discovery is based on empiricism, which makes the discovery process time-consuming and expensive. There is an urgent need to develop effective approaches to discover efficient catalysts for hydrogenation reactions. In this work, we explore the approach of machine learning to predict outcomes of catalytic hydrogenation of esters using various ML architectures – NN, GP, decision tree, random forest, KNN, and linear regression. Our optimized models can predict the reaction yields with reasonable error for example, a root mean square error (RMSE) of 11.76% using GP on unseen data and suggest that the use of certain chemical descriptors (e.g. electronic parameters) selectively can result in a more accurate model. Furthermore, studies have also been carried out for the prediction of catalysts and reaction conditions such as temperature and pressure as well as their validation by performing hydrogenation reactions to improve the poor yields described in the dataset.