Evaluation of a Data-Driven, Machine Learning Approach for Identifying Potential Candidates for Environmental Catalysts: From Database Development to Prediction
Yulong Chen, Rong Li, Hongri Suo, Chongxuan Liu
Abstract
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 NOx 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.