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Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

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Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
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Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA
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State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, and International Research Center for Computational Mechanics, Dalian University of Technology, Dalian 116023, China
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Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
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Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA
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Authors to whom correspondence should be addressed.
Polymers 2020, 12(1), 163; https://doi.org/10.3390/polym12010163
Received: 1 December 2019 / Revised: 27 December 2019 / Accepted: 2 January 2020 / Published: 8 January 2020
(This article belongs to the Special Issue Artificial Intelligence in Polymer Science and Chemistry)
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. View Full-Text
Keywords: de novo materials design; machine learning; data-driven algorithm; organic molecules; polymers; materials database de novo materials design; machine learning; data-driven algorithm; organic molecules; polymers; materials database
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Chen, G.; Shen, Z.; Iyer, A.; Ghumman, U.F.; Tang, S.; Bi, J.; Chen, W.; Li, Y. Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges. Polymers 2020, 12, 163.

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