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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and, in particular, machine learning (ML) as a subcategory of AI, provides unique opportunities for the discovery and development of innovative polymers and organic molecules. In the past, the development of polymers and organic molecules traditionally has been a trail-and-error process, guided by experience of experts, human intuition, and conceptual insights. However, such an approach is usually slow, costly and biased towards certain domains of chemical space, and limited to relatively small-scale studies. In addition, automation of organic molecules and materials design is considerably less developed than that for inorganic materials due to challenges associated with searching the vast design space (on the order of 10⁶⁰–10¹⁰⁰) defined by the almost infinite combinations of molecular constituent, microstructures, and synthesis conditions. Very recently, various ML approaches have emerged, some of which have been successfully employed for the de novo design of polymers and organic molecules. This Special Issue will address recent experimental, computational, and theoretical advances in this burgeoning field. Topics of particular interest include but are not limited to: (a) ML-assisted discovery and design of innovative polymers and organic molecules; (b) data-driven methods for design, synthesis, and characterization of polymers and their composites; (c) ML-enabled physical and mechanistic insights into polymer physics and chemistry; (d) deep insights into chemistry–structure–property–performance relation of polymers revealed by ML techniques; and (e) ML-accelerated multiscale modeling approach for polymers and polymer composites. The goal of this Special Issue is to bring together researchers from a variety of backgrounds to exchange ideas, identify and address grand challenges, and initiate new areas of research in this burgeoning field.

Dr. Ying Li
Guest Editor

Manuscript Submission Information

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Keywords

Artificial Intelligence
Machine learning
Data-driven approach
Polymer design
Polymer synthesis
Polymer physics
Polymer mechanics
Structure–property relation
Multiscale modeling

Published Papers (2 papers)

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Research

Jump to: Review

14 pages, 1466 KiB

Open AccessArticle

Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model

by Guang Chen, Lei Tao and Ying Li

Polymers 2021, 13(11), 1898; https://0-doi-org.brum.beds.ac.uk/10.3390/polym13111898 - 07 Jun 2021

Cited by 35 | Viewed by 7582

Abstract

We propose a chemical language processing model to predict polymers’ glass transition temperature (

T_{g}

We propose a chemical language processing model to predict polymers’ glass transition temperature (

T_{g}

) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of a polymer’s repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point ‘*’. Results show that the trained model demonstrates reasonable prediction performance on unseen polymer’s

T_{g}

. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of polymer

T_{g}

. The framework of this model is general and can be used to construct structure–property relationships for other polymer properties. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymer Science and Chemistry)

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Review

Jump to: Research

45 pages, 23366 KiB

Open AccessEditor’s ChoiceReview

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

by Guang Chen, Zhiqiang Shen, Akshay Iyer, Umar Farooq Ghumman, Shan Tang, Jinbo Bi, Wei Chen and Ying Li

Polymers 2020, 12(1), 163; https://0-doi-org.brum.beds.ac.uk/10.3390/polym12010163 - 08 Jan 2020

Cited by 98 | Viewed by 19162

Abstract

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. Full article

(This article belongs to the Special Issue Artificial Intelligence in Polymer Science and Chemistry)

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