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Multiobjective Optimization of Metal-Organic Framework Structural Properties and Synthesis Costs through Machine

Huafang Zhang1, Chunjian Pan2, Qi Liang1

  • 1School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.

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|December 12, 2025
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This study introduces a machine learning framework to optimize metal-organic framework (MOF) synthesis, balancing high surface area (SSA) with reduced costs. The approach identifies cost-effective, high-performance MOF production routes.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Metal-organic frameworks (MOFs) offer high surface area and tunable porosity for diverse applications.
  • Current MOF synthesis methods are hindered by high costs and lengthy reaction times.
  • Synthesis conditions significantly influence MOF structural properties and cost-effectiveness.

Purpose of the Study:

  • To develop a machine learning-based multiobjective framework for optimizing MOF synthesis.
  • To identify synthesis conditions that maximize specific surface area (SSA) while minimizing production costs.
  • To enable the commercialization of high-performance, low-cost MOFs.

Main Methods:

  • Utilized the SynMOF database and commercial raw material cost data.
  • Developed machine learning predictive models for MOF properties and costs.
  • Employed nondominated sorting genetic algorithm II (NSGA-II) for Pareto-optimal route identification.
  • Applied k-Nearest Neighbors (k-NN) for practical condition retrieval.
  • Screened solutions using a Bagging-integrated positive-unlabeled learning model for production feasibility.

Main Results:

  • Identified Pareto-optimal synthesis routes maximizing SSA and minimizing cost.
  • Retrieved practical synthesis conditions matching desired MOF features.
  • Screened for solutions with high production feasibility (>0.7 probability).

Conclusions:

  • The proposed machine learning framework offers a strategic pathway for cost-effective MOF synthesis.
  • This approach facilitates the development of high-performance MOFs for commercial applications.
  • Optimizing synthesis conditions is crucial for balancing MOF performance and economic viability.