Establishing the structure–property relationship by machine learning (ML) models is
extremely valuable for accelerating the molecular design of polymers. However, existing ML …

Thermal stability is an invaluable aspect in assessing polymer properties, especially for
polyimides (PIs), which are known for their excellent heat resistance. However, empirically …

The construction of material prediction models using machine learning algorithms can aid in
the polyimide structural design and screening of materials as well as accelerate the …

Machine learning models have gained prominence for predicting pure-component
properties, yet their application to mixture property prediction remains relatively limited …

Over the past few decades, the enhancement of polymer thermal conductivity has attracted
considerable attention in the scientific community due to its potential for the development of …

Machine-learning models are increasingly used to predict properties of atoms in chemical
systems. There have been major advances in developing descriptors and regression …

Proton conductivity (σ) and hydration number (λ) are important characteristics for proton
exchange membranes (PEMs) in fuel cells and water electrolyzers. A High σ yields high …

To address the issues with molecular representation of copolymerized polyimides (PIs) and
the mini dataset of PI powders. We constructed an interpretable machine learning (ML) …

Artificial neural networks (ANNs), a subset of Quantitative Structure-Property Relationship
(QSPR) methods, offer a promising avenue for addressing challenges in materials science …

Abstract We introduce HAPPY (Hierarchically Abstracted rePeat unit of PolYmers), a string
representation for polymers, designed to efficiently encapsulate essential polymer structure …