2025-03-17 オランダ・デルフト工科大学 (TUDelft)
＜関連情報＞
- https://www.tudelft.nl/en/2025/me/news/improving-recyclability-of-polymers-machine-learning-helps-finding-needle-in-haystack
- https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202411385
AIによるリサイクル可能なビトリマーポリマーの逆設計と発見 AI-Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers
Yiwen Zheng, Prakash Thakolkaran, Agni K. Biswal, Jake A. Smith, Ziheng Lu, Shuxin Zheng, Bichlien H. Nguyen, Siddhant Kumar, Aniruddh Vashisth
Advanced Science Published: 16 December 2024
DOI:https://doi.org/10.1002/advs.202411385
Abstract
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.
