TY - JOUR
T1 - Machine-learning-assisted de novo design of organic molecules and polymers
T2 - Opportunities and challenges
AU - Chen, Guang
AU - Shen, Zhiqiang
AU - Iyer, Akshay
AU - Ghumman, Umar Farooq
AU - Tang, Shan
AU - Bi, Jinbo
AU - Chen, Wei
AU - Li, Ying
N1 - Funding Information:
W.C. would like to acknowledge the support from NSF grants (CMMI-1729743, EEC-1530734, and CMMI-1662435) and Center for Hierarchical Materials Design 70NANB19H005. Y.L. would like to thank the support from NSF grants (OAC-1755779, CMMI-1762661 and CMMI-1934829).
Funding Information:
Acknowledgments: We thankfully acknowledge the National Research Foundation of Korea grant funded by the Government of Korea (NO.2018R1A6A1A03025582) for the financial support.
Publisher Copyright:
© 2020 by the authors.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.
AB - Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.
KW - Data-driven algorithm
KW - De novo materials design
KW - Machine learning
KW - Materials database
KW - Organic molecules
KW - Polymers
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U2 - 10.3390/polym12010163
DO - 10.3390/polym12010163
M3 - Article
C2 - 31936321
AN - SCOPUS:85078431940
VL - 12
JO - Polymers
JF - Polymers
SN - 2073-4360
IS - 1
M1 - 163
ER -