Recent advances in utility of artificial intelligence towards multiscale colloidal based materials design

Colloidal material design necessitates a collection of computer approaches ranging from quantum chemistry to molecular dynamics and continuum modeling. Machine learning (ML) and other umbrella terminology for current optimization approaches (requiring computation) have accelerated the predictability of material characteristics. Colloidal materials include polymers, liquid crystals, and colloids. Supervised and unsupervised strategies have come under scrutiny in this review. Other ways, such as combined simulation of ML and molecular modeling dynamics procedures, are also available that are not available through the present arsenal of characterization tools. Such hybrid approaches can improve our understanding of materials and design protocols. In this review, we have accumulated expertise and information from over 300 sources.

Automated summary

Colloidal material design involves a variety of computer approaches, including quantum chemistry, molecular dynamics, and continuum modeling. Machine learning and other optimization methods have accelerated the predictability of material characteristics, while combined simulations and molecular modeling dynamics procedures have expanded the arsenal of characterization tools. These hybrid methods can improve our understanding of materials and design protocols.