Accelerated identification of high-performance catalysts for low-temperature NH3-SCR by machine learning
Accelerated identification of high-performance catalysts for low-temperature NH3-SCR by machine learning
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DOI：
10.1039/d1ta06772a
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发表时间：
2021-10-11
影响因子：
11.9
通讯作者：
Gao, Xiang
中科院分区：
文献类型：
--
作者：
Dong, Yi;Zhang, Yu;Gao, Xiang
已经投入了大量的努力来开发用于去除环境污染物的催化剂。然而，通过试错筛选催化剂消耗大量时间和资源。在这里，我们提出了一种机器学习方法，用于基于覆盖2000多个相关报告的定制数据库的选择性催化还原（SCR）催化剂发现。选择催化剂特性和工作条件作为预测催化剂活性的特征。额外的树回归模型被确定为最好的性能在这里为此目的检查的八个算法。具有强氧化能力的元素，如锰，作为活性组分，被发现是重要的开发优良的低温SCR催化剂使用计算的特征重要性分数。利用优化后的机器学习模型对Mn基SCR催化剂进行了辅助识别，Mn-Ce-Co催化剂在150-300 ℃的宽温度范围内NO转化率大于80%。
Significant efforts have been devoted to the development of catalysts for the removal of environmental pollutants. However, screening catalysts through trial and error consumes a lot of time and resources. Here we present a machine learning approach for selective catalytic reduction (SCR) catalyst discovery based on a custom-build database covering over 2000 related reports. Catalyst characteristics and working conditions were selected as features to predict catalyst activities. The extra tree regression model was identified as the best performer among the eight algorithms examined here for this purpose. Elements with strong oxidizing ability, such as Mn, as the active component were found to be important for developing excellent low temperature SCR catalysts using the calculated feature importance scores. The optimized machine learning model was used to aid the identification of Mn-based SCR catalysts and the NO conversion rate of the Mn-Ce-Co catalyst is greater than 80% in a wide temperature range of 150-300 degrees C.