Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data
Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data
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DOI：
10.1002/cctc.201900971
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发表时间：
2019-08-19
期刊：
影响因子：
4.5
通讯作者：
Takigawa, Ichigaku
中科院分区：
文献类型：
--
作者：
Suzuki, Keisuke;Toyao, Takashi;Takigawa, Ichigaku
这些文献为催化剂的设计和发现提供了见解。使用机器学习（ML）方法对报告数据进行有效分析，可以获得有价值的信息。然而，以这种方式利用文献存在障碍，例如缺乏成分重叠，来自先前发表的数据的偏差，以及许多元素的低样本计数。本研究描述了一种ML方法，认为元素特征作为输入表示，而不是直接输入催化剂组合物。这种ML方法具有催化剂发现的潜力，包括在可用数据中具有有限催化剂组合物重叠的催化反应。选择甲烷氧化偶联（OCM），水煤气变换（WGS），和CO氧化反应，以确认所提出的方法的有效性，通过分析使用几个国家的最先进的ML方法。在测试的ML方法中，XGBoost（XGB）的梯度提升回归提供了最好的结果，并且所提出的方法提高了所有三种反应类型的预测精度。此外，计算了“特征重要性得分”的定量值，以评估对催化剂性能影响最大的输入变量。最后，使用ML作为“替代”模型探索催化剂优化，并基于优化确定了OCM反应的前20个有前途的候选催化剂。探讨了ML在催化分析中的优势以及多相催化复杂性带来的困难和局限性。
The literature provides insights for catalyst design and discovery. Effective analysis of reported data using machine learning (ML) methods offers the ability to gain valuable information. However, utilizing the literature in this way has obstacles such as lack of compositional overlaps, bias from prior published data, and low sample counts for many elements. The present study describes an ML approach that considers elemental features as input representations instead of inputting catalyst compositions directly. This ML method has the potential for catalyst discovery, including catalytic reactions with limited catalyst composition overlap in the available data. Oxidative coupling of methane (OCM), water gas shift (WGS), and CO oxidation reactions were chosen to confirm the effectiveness of the proposed method by analysis using several state-of-the-art ML methods. Among the ML methods tested, gradient boosting regression with XGBoost (XGB) provided the best results, and prediction accuracy was improved by the proposed approach for all three reaction types. In addition, a quantitative value of "feature importance score" was calculated to evaluate the most influential input variables on catalyst performance. Finally, catalyst optimization was explored using ML as a "surrogate" model, and the top 20 promising candidate catalysts were identified for the OCM reaction based on the optimization. The advantages of ML in catalysis analysis as well as the difficulties and limitations originating from the complexity of heterogeneous catalysis were explored.