A machine learning-based multi-objective optimization method for MOFs synthesis routes

By optimizing the MOF synthesis route through machine learning, the problems of long synthesis cycle and high cost of MOF catalytic materials have been solved, and the defect content and thermal stability of materials have been synergistically improved, thereby improving R&D efficiency and material performance.

CN116453627BActive Publication Date: 2026-05-29UNIV OF SCI & TECH BEIJING
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Patent Information

Application Number
CN202310283468.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-22
Publication Date
2026-05-29
Estimated Expiration
2043-03-22

AI Technical Summary

Technical Problem

Existing technologies for preparing MOF catalytic materials suffer from problems such as long synthesis cycles, high costs, and low efficiency. In particular, there is a lack of efficient experimental methods when simultaneously optimizing the defect content and thermal stability of the materials.

Method used

By employing a multi-objective optimization strategy using machine learning, and by collecting and analyzing MOF synthesis condition data, eight machine learning algorithms are used for modeling, surrogate models are selected, and synthesis conditions are optimized to achieve a synergistic improvement in material defect content and thermal stability.

Benefits of technology

This enables rapid and low-cost synthesis of MOF materials, improves catalytic activity and thermal stability, reduces experimental waste, and shortens the research and development cycle.

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Abstract

The application discloses a MOFs synthesis route multi-objective optimization method based on machine learning. The method is to collect the synthesis conditions of prepared Ce-UiO-66, and evaluate the defect content and thermal stability thereof through a thermogravimetric analysis curve, as initial data set; the data set is randomly divided into a training set and a test set, eight algorithms are adopted to model each performance of Ce-UiO-66 and select the proxy model for performance prediction; the target achievement probability (PA) value of each performance in the synthesis space is calculated and expanded into a multi-objective evaluation factor; the Ce-UiO-66 material is prepared; the obtained material is subjected to characterization test, if the test data does not meet the requirement, the data set and the proxy model are updated. The application has the advantages of low cost, short cycle and the like in optimizing the catalytic and stable performances of MOFs based on reliable experimental data and machine learning, and can be popularized to the design of synthesis routes of other materials.
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