Data-driven, machine learning approaches are increasingly used for the discovery and development of catalytic materials in the area of material science and engineering. In this paper, the approach was evaluated with respect to its applicability in identifying potential environmental catalysts (ECs) using the selective catalytic reduction (SCR) of the air pollutant NO_x as an example. The detailed procedures including database assemblage, the training and testing of a machine learning model, the validation and prediction of the model, and model uncertainties are all provided. The results indicated that there is a significant amount of data accumulated in environmental catalysts that can be exploited for accelerating the exploration and optimization of ECs for specific applications. The results also indicated that the approach is powerful for identifying new ECs and optimizing conditions for ECs synthesis and applications. However, the reported data in the literature are often incomplete, which limits the application potentials of the data. With limited data, the simulated results from the model contained uncertainties, especially in the prediction of unknown ECs. Repeated predictions and ensemble averaging were then proposed as an approach to find conditions for synthesizing and applying promising ECs.

中文翻译：

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评估数据驱动的机器学习方法，用于识别环境催化剂的潜在候选者：从数据库开发到预测

数据驱动的机器学习方法越来越多地用于材料科学和工程领域催化材料的发现和开发。在本文中，评估了该方法在使用空气污染物 NO _x的选择性催化还原 (SCR) 识别潜在环境催化剂 (EC) 中的适用性。举个例子。提供了包括数据库组装、机器学习模型的训练和测试、模型的验证和预测以及模型不确定性在内的详细过程。结果表明，在环境催化剂中积累了大量数据，可用于加速针对特定应用的 ECs 的探索和优化。结果还表明，该方法对于识别新的 ECs 和优化 ECs 合成和应用的条件是强大的。然而，文献报道的数据往往不完整，限制了数据的应用潜力。由于数据有限，模型的模拟结果包含不确定性，特别是在未知 ECs 的预测中。