Journal
SOFT MATTER
Volume 17, Issue 33, Pages 7607-7622Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1sm00725d
Keywords
-
Categories
Funding
- National Science Foundation [1825352, 1933861]
- United States Department of Agriculture [2020-67030-31336]
- Sony Research Award Program 2019
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [1933861] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1825352] Funding Source: National Science Foundation
Ask authors/readers for more resources
This review discusses the application of data-driven methods in polymer design and the development and implementation of inverse design strategies. Through data-driven strategies such as representation of polymers, high-throughput virtual screening, global optimization, and generative models, reverse design of polymers has been achieved. The review also highlights the challenges and opportunities of this approach.
The ever-increasing demand for novel polymers with superior properties requires a deeper understanding and exploration of the chemical space. Recently, data-driven approaches to explore the chemical space for polymer design have emerged. Among them, inverse design strategies for designing polymers with specific properties have evolved to be a significant materials informatics platform by learning hidden knowledge from materials data as well as smartly navigating the chemical space in an optimized way. In this review, we first summarize the progress in the representation of polymers, a prerequisite step for the inverse design of polymers. Then, we systematically introduce three data-driven strategies implemented for the inverse design of polymers, i.e., high-throughput virtual screening, global optimization, and generative models. Finally, we discuss the challenges and opportunities of the data-driven strategies as well as optimization algorithms employed in the inverse design of polymers.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available