Work

Bridging scales: machine learning for the rational design and modelling of shape memory polymers

Year: 2025

Type: article

Abstract: , by incomplete structural representations and challenges in integrating thermal and temporal effects into the neural network. Finally, we outline future directions to explore and implement, including developing tools to capture complex SMP topologies and creating polymer-specific neural networks. The discussion allows us to provide new insights into the use of ML tools for the world of SMPs.

Source: Soft Matter

DOI: 10.1039/d5sm00980d

Authors: Cheng Yan, Giulia Scalet

Institutions: Southern University and Agricultural and Mechanical College, University of Pavia

Language: English

Topic: Advanced Materials and Mechanics

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid