Iterative Approach of Experiment-Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO x Selective Reduction Catalysts

Chen, Yulong; Feng, Jia; Wang, Xin; Zhang, Cheng; Ke, Dongfang; Zhu, Huiyan; Wang, Shuai; Suo, Hongri; Liu, Chongxuan

2023-07-01

10.1021/acs.est.3c00293

ENVIRONMENTAL SCIENCE & TECHNOLOGY   影响因子和分区

0013-936X

1520-5851

["Minimal research works on the application of artificialintelligence technology in environmental catalyst development. Thisstudy reports an iteration approach of a data-driven model with labexperiments to develop a novel catalyst of atmospheric pollutantsrapidly.","An iterative approachbetween machine learning (ML) and laboratoryexperiments was developed to accelerate the design and synthesis ofenvironmental catalysts (ECs) using selective catalytic reduction(SCR) of nitrogen oxides (NO x ) as an example.The main steps in the approach include training a ML model using therelevant data collected from the literature, screening candidate catalystsfrom the trained model, experimentally synthesizing and characterizingthe candidates, updating the ML model by incorporating the new experimentalresults, and screening promising catalysts again with the updatedmodel. This process is iterated with a goal to obtain an optimizedcatalyst. Using the iterative approach in this study, a novel SCRNO x catalyst with low cost, high activity,and a wide range of application temperatures was found and successfullysynthesized after four iterations. The approach is general enoughthat it can be readily extended for screening and optimizing the designof other environmental catalysts and has strong implications for thediscovery of other environmental materials."]

environmental catalysts machine learning iteration between machine learning and experiments nitrogenoxides selective catalytic reduction

SCI ; EI

英语

第一 ; 通讯

Department of Science and Technology of Guangdong Province[2017ZT07Z479] ; National Natural Science Foundation of China[42007318]

Engineering ; Environmental Sciences & Ecology

Engineering, Environmental ; Environmental Sciences

WOS:001018802000001

AMER CHEMICAL SOC

20232914399240

Catalysts ; Machine learning ; Nitrogen oxides

Air Pollution Control:451.2 ; Artificial Intelligence:723.4 ; Chemical Agents and Basic Industrial Chemicals:803 ; Chemical Products Generally:804 ; Inorganic Compounds:804.2

ENVIRONMENT/ECOLOGY

Web of Science

Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen 518055, Guangdong, Peoples R China

Chen, Yulong,Feng, Jia,Wang, Xin,et al. Iterative Approach of Experiment-Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO x Selective Reduction Catalysts[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2023,57:18080-18090.

Chen, Yulong.,Feng, Jia.,Wang, Xin.,Zhang, Cheng.,Ke, Dongfang.,...&Liu, Chongxuan.(2023).Iterative Approach of Experiment-Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO x Selective Reduction Catalysts.ENVIRONMENTAL SCIENCE & TECHNOLOGY,57,18080-18090.

Chen, Yulong,et al."Iterative Approach of Experiment-Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO x Selective Reduction Catalysts".ENVIRONMENTAL SCIENCE & TECHNOLOGY 57(2023):18080-18090.