The integration of machine learning into hydroformylation processes represents a pivotal advancement in high-throughput screening within the chemical industry. This study employs a data-driven approach to develop predictive models for terminal olefin reactions. Using a database of 1,167 entries, we merged reaction embeddings with corresponding labels. The well-trained extreme gradient boosting model achieves a test set coefficient of determination (R2) score of 0.897. However, when applied to specific-olefin tasks, the model shows suboptimal performance. Therefore, tailored models for specific olefins like 1-octene and styrene are developed, achieving improved test set R2 scores of 0.850 and 0.789, respectively, compared to the general-olefin task. Interpretability findings highlight the significance of high-temperature, low-pressure, and low-concentration metals in enhancing linear regioselectivity and providing chemical insights. This study underscores the transformative potential of machine learning as a surrogate model in advancing high-throughput screening and optimizing chemical processes in the industry.

中文翻译：

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用于区域选择性预测的末端烯烃加氢甲酰化的个性化机器学习模型


将机器学习集成到加氢甲酰化过程中代表了化学行业高通量筛选的关键进步。本研究采用数据驱动的方法来开发末端烯烃反应的预测模型。使用包含 1,167 个条目的数据库，我们将反应嵌入与相应的标签合并。训练有素的极端梯度增强模型的测试集决定系数 (R2) 得分为 0.897。然而，当应用于特定烯烃任务时，该模型表现出次优性能。因此，开发了针对 1-辛烯和苯乙烯等特定烯烃的定制模型，与一般烯烃任务相比，测试集 R2 分数分别提高了 0.850 和 0.789。可解释性研究结果强调了高温、低压和低浓度金属在增强线性区域选择性和提供化学见解方面的重要性。这项研究强调了机器学习作为替代模型在推进高通量筛选和优化行业化学工艺方面的变革潜力。