Machine learning discovery of high-temperature polymers

Tao, Lei; Chen, Guang; Li, Ying

doi:10.1016/j.patter.2021.100225

Cited by 56 publications

(75 citation statements)

References 92 publications

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“…This performance is reasonably well and comparable with many other ML models for

prediction in terms of MAE values or

score [ 24 , 37 , 38 , 39 , 79 ], which confirms the effectiveness of the chemical language processing model. Note that in most previous works, the polymer samples were not large and only certain types of polymers were studied [ 36 ], the MAE and

score may be higher. While in this work, the data size is very large and the types of polymers in the database are very general.…”

Section: Resultsmentioning

confidence: 99%

“…To demonstrate the capability of our chemical language processing model, another unlabeled polymer dataset of 5686 samples without reported

values are considered for a high-throughput screening task. This dataset collected from earlier work [ 36 ] is also from the PolyInfo database [ 48 ]. Thus, these two databases are considered similar.…”

Section: Resultsmentioning

confidence: 99%

“…The main reason is that the database of polymers with high quality is very limited. In polymer literature, the database in most of previous studies were under a few hundreds or even less [ 36 ]. Therefore, DL models were not widely applied in these studies.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Tao

2021

Polymers

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We propose a chemical language processing model to predict polymers’ glass transition temperature (Tg) 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 Tg. 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 Tg. The framework of this model is general and can be used to construct structure–property relationships for other polymer properties.

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“…This performance is reasonably well and comparable with many other ML models for

prediction in terms of MAE values or

score may be higher. While in this work, the data size is very large and the types of polymers in the database are very general.…”

Section: Resultsmentioning

confidence: 99%

“…To demonstrate the capability of our chemical language processing model, another unlabeled polymer dataset of 5686 samples without reported

Section: Resultsmentioning

confidence: 99%

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

Chen

Tao

2021

Polymers

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show abstract

“…The integration of CGMD/ML is able to speed up the prediction of polymer properties at the chain level. Researchers have adopted ML models for the prediction of polymer properties mostly based on their monomer representation ( Ramprasad and Kim, 2019 ; Sattari et al, 2021 ; Chen et al, 2021b ; Gracheva et al, 2021 ), ignoring the influence of polymer chains, such as molecular weight, topology ( Tao et al, 2021a ), and copolymer sequence ( Kuenneth et al, 2021 ). Particularly for novel polymeric materials, there are limitations in the existing database due to unexplored chemical space ( Wilbraham et al, 2019 ).…”

Section: Application Of ML For Understanding and Design Of Polymer Chainsmentioning

confidence: 99%

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

2022

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In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics—polymeric configuration characterization, feed-forward property prediction, and inverse design—in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.

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“…The for a high-throughput screening task. This dataset collected from earlier work [36] is 265 also from the PolyInfo database [48]. Thus, these two databases are considered similar.…”

mentioning

confidence: 99%

Predicting Polymer’s Glass Transition Temperature by A Chemical Language Processing Model

Chen¹,

Lei²,

Li³

2021

Preprint

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We propose a chemical language processing model to predict polymers’ glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of 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 and simple. 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 accuracy on unseen polymer’s Tg. 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’s repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer’s properties.

show abstract

Machine learning discovery of high-temperature polymers

Cited by 56 publications

References 92 publications

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

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

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

Predicting Polymer’s Glass Transition Temperature by A Chemical Language Processing Model

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