Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO2 capture, CH4 storage, gas separation, catalysis, etc. Traditional methods of material research, which mainly rely on manual experimentation, are not particularly efficient, while with advancements in computer science, high-throughput computational screening methods based on molecular simulation have become crucial in material discovery, yet they face limitations in terms of computational resources and time. Currently, machine learning (ML) has emerged as a transformative tool in many…

• NS
  Natural Science Foundation of Shanghai

  Award: 24ZR1453900
• SM
  Shanghai Municipal Education Commission

  Award: Z-2024-369-036
• SA
  Science and Technology Commission of Shanghai Municipality

  Award: 22142200800