Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework

Yan, Cheng; Feng, Xiaming; Konlan, John; Mensah, Patrick; Li, Guoqiang

doi:10.1039/d3cp03631f

Phys. Chem. Chem. Phys.

2023

DOI: 10.1039/d3cp03631f

|View full text |Cite

Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework

Cheng Yan,

Xiaming Feng,

John Konlan

et al.

Abstract: Shape memory vitrimers (SMVs) are an emerging class of advanced materials that have garnered significant interest from researchers in the past five to six years. These materials can return to...

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

AI‐Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers

Zheng,

Thakolkaran,

Biswal

et al. 2024

Advanced Science

View full text Add to dashboard Cite

Vitrimer is a new, exciting class of sustainable polymers with healing abilities due to their dynamic covalent adaptive networks. However, a limited choice of constituent molecules restricts their property space and potential applications. To overcome this challenge, an innovative approach coupling molecular dynamics (MD) simulations and a novel graph variational autoencoder (VAE) model for inverse design of vitrimer chemistries with desired glass transition temperature (Tg) is presented. The first diverse vitrimer dataset of one million chemistries is curated and Tg for 8,424 of them is calculated by high‐throughput MD simulations calibrated by a Gaussian process model. The proposed VAE employs dual graph encoders and a latent dimension overlapping scheme which allows for individual representation of multi‐component vitrimers. High accuracy and efficiency of the framework are demonstrated by discovering novel vitrimers with desirable Tg beyond the training regime. To validate the effectiveness of the framework in experiments, vitrimer chemistries are generated with a target Tg = 323 K. By incorporating chemical intuition, a novel vitrimer with Tg of 311–317 K is synthesized, experimentally demonstrating healability and flowability. The proposed framework offers an exciting tool for polymer chemists to design and synthesize novel, sustainable polymers for various applications.

show abstract

AI‐Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers

Zheng,

Thakolkaran,

Biswal

et al. 2024

Advanced Science

View full text Add to dashboard Cite

show abstract

Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders

Teimouri,

2024

Journal of Polymer Science

View full text Add to dashboard Cite

The discovery of high‐performance shape memory polymers (SMPs) with enhanced glass transition temperatures (Tg) is of paramount importance in fields such as geothermal energy, oil and gas, aerospace, and other high‐temperature applications, where materials are required to exhibit shape memory effect at extremely high‐temperature conditions. Here, we employ a novel machine learning framework that integrates transfer learning with variational autoencoders (VAE) to efficiently explore the chemical design space of SMPs and identify new candidates with high Tg values. We systematically investigate the effect of different latent space dimensions on the VAE model performance. Several machine learning models are then trained to predict Tg. We find that the SVM model demonstrates the highest predictive accuracy, with R2 values exceeding 0.87 and a mean absolute percentage error as low as 6.43% on the test set. Through systematic molar ratio adjustments and VAE‐based fingerprinting, we discover novel SMP candidates with Tg values between 190°C and 200°C, suitable for high‐temperature applications. These findings underscore the effectiveness of combining VAEs and SVM for SMP discovery, offering a scalable and efficient method for identifying polymers with tailored thermal properties.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework

Abstract: Shape memory vitrimers (SMVs) are an emerging class of advanced materials that have garnered significant interest from researchers in the past five to six years. These materials can return to...

Cited by 2 publications

References 51 publications

AI‐Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers

AI‐Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers

Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders

Contact Info

Product

Resources

About