A method, device and medium for predicting the photocatalytic water splitting performance of 2D-COF materials

By establishing the 2D-COF photocatalytic performance data set and screening out excellent photocatalytic materials using GBR algorithm, the problem of high cost and long period of catalytic performance prediction of existing 2D-COF materials is solved, and low-cost and efficient catalytic performance prediction is achieved.

CN119418815BActive Publication Date: 2025-09-05HARBIN UNIV OF SCI & TECH
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Patent Information

Application Number
CN202411536717.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-31
Publication Date
2025-09-05
Estimated Expiration
2044-10-31

AI Technical Summary

Technical Problem

The existing 2D-COF material catalytic performance prediction methods are expensive, have long cycles, and have single results, and lack low-cost and efficient prediction technology.

Method used

Establish a 2D-COF photocatalytic full water-removing performance data set, and automatically generate material structure data files, and combine the machine learning algorithm GBR to screen out 2D-COF materials with excellent photocatalytic performance, including building electronic structure data sets and chemical feature data sets, and use GBR algorithm to make multiple predictions to screen out excellent photocatalytic materials.

Benefits of technology

It has achieved low-cost and efficient prediction of the photocatalytic properties of 2D-COF materials, solved the problems of high cost and long cycle in traditional methods, and improved the efficiency of material discovery.

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Abstract

The present invention discloses a method, device and medium for predicting the performance of photocatalytic water splitting of 2D-COF materials, which relate to the fields of catalytic technology and material information technology. The method comprises: first, converting the material structure into crystal structure data represented by atomic species and coordinates. In view of the large number of 2D-COF material types, a large number of material structure data files are produced using automated file generation technology. Then, material structure characteristic data are generated through experimental testing. Next, the structural data file is subjected to first-principles calculations to generate performance characteristic data. Finally, machine learning technology is used to realize the prediction of the catalytic properties of 2D-COF materials. Since the catalytic performance indicators of photocatalytic water splitting include three types of indicators for distinguishing material electronic structure values, reaction free energy values ​​and overpotential values, the above process can be repeated to perform three predictions, and excellent photocatalytic water splitting 2D-COF materials can be screened out.
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