论文标题
使用机器学习预测双金属金属催化剂的活性和选择性
Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning
论文作者
论文摘要
机器学习非常适合大型均匀数据集中的模式检测，但是催化剂研究的一致实验数据集通常很小。在这里，我们演示了如何使用机器学习和第一原理计算的组合来从一组相对较小的实验数据中提取知识。该方法基于结合一个复杂的机器学习模型，该模型在过渡状态能量的计算库中训练，并与文献中的实验催化活性和选择性的简单线性回归模型相结合。使用合并的模型，我们确定了乙醇重整涉及的关键C-C键分裂反应，并对具有架构的单层双金属催化剂进行乙醇重整进行计算筛选，该催化剂具有架构TM-PT-PT-PT-PT-PT（111）和PT-TM-PT（111）（111）（TM = 3D过渡金属）。该模型还预测了未来实验研究的四个有前途的催化剂组成。该方法不仅限于乙醇的改革，而在于解释实验观察以及催化材料的计算发现。
Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on a computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C-C bond scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM-Pt-Pt(111) and Pt-TM-Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. The approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of catalytic materials.
