Predicting molecular ordering in a binary liquid crystal using machine learning

Engineering & Materials Science

• Liquid crystals 99%
• Machine learning 57%
• Functional materials 36%
• Superconducting transition temperature 28%
• Phase transitions 27%
• Molecules 25%
• Physical properties 23%
• Self assembly 17%
• Materials science 16%
• Chemical properties 14%
• Surface active agents 14%
• Product development 13%
• Chemical reactions 13%
• Nanoparticles 12%
• Copper 10%
• Industry 5%

Physics & Astronomy

• machine learning 100%
• liquid crystals 61%
• transition temperature 20%
• physical properties 20%
• product development 19%
• materials science 14%
• molecules 13%
• self assembly 13%
• chemical properties 13%
• surfactants 12%
• chemical reactions 11%
• industries 10%
• copper 9%
• cycles 8%
• products 8%
• predictions 7%

Chemical Compounds

• Liquid Crystal 73%
• Phase Transition Temperature 58%
• Crystal System 29%
• Industry 19%
• Molecule 18%
• Surfactant 17%
• Nanoparticle 13%
• Energy 12%
• Application 8%