A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials

Keyphrases

• Machine Learning 100%
• Carbon Materials 100%
• Nanoarchitecture 100%
• Porous Carbon Materials 100%
• Energy Storage 66%
• Machine Learning Applications 66%
• Carbon Capture 66%
• Materials Science 33%
• Sustainable Materials 33%
• Rapid Development 33%
• Environmental Challenges 33%
• Trial-and-error 33%
• Porous Materials 33%
• Data-driven Methods 33%
• Learning Process 33%
• International Environmental Policy 33%
• Energy Efficiency Improvement 33%
• Gas Separation 33%
• Pore Volume 33%
• Supercapacitor 33%
• Carbon Emission Reduction 33%
• Effective Material 33%
• Artificial Intelligence Learning 33%
• Energy Challenge 33%
• Porous Carbon 33%
• Energy Catalysis 33%
• Pore Shape 33%
• Carbon Application 33%
• New Material Development 33%
• Efficient Development 33%
• Applications in the Field 33%
• Latest Advancements 33%
• Surface Pores 33%
• Capture Energy 33%
• Separation Energy 33%
• Tar pitch 33%
• Coal Tar pitch 33%
• Biomass Precursor 33%
• Carbon Synthesis 33%

Material Science

• Porous Carbon 100%
• Supercapacitors 25%
• Catalysis 25%
• Pore Volume 25%

Chemical Engineering

• Learning System 100%
• Artificial Intelligence 20%