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

In copolymerization, the monomers' reactivity ratios play an important role in shaping the
final copolymer properties. Thus, the knowledge of reactivity ratio is essential to the polymer …

Conjugated polymers have garnered significant attention due to their diverse applications in
electronics, photonics, and energy storage. However, realizing their full potential poses a …

The design and development of polymeric materials have been a hot domain for decades.
However, traditional experiments and molecular simulations are time-consuming and labor …

Monomer reactivity ratios are central to our understanding of the polymerization rate,
copolymer composition and sequence distribution of copolymers produced by radical …

An important application of machine learning techniques is the intelligent nondestructive
testing of polymers. However, data scarcity and class imbalance (for real applications) …

The glass transition temperature of polymers is a key parameter in meeting the application
requirements for energy absorption. Previous studies have provided some data from slow …

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

Here, we demonstrate how machine learning enables the prediction of comonomers
reactivity ratios based on the molecular structure of monomers. We combined multi-task …

Sustainable engineering is of great importance for resilient and agile technology and
society. This book balances economics, environment, and societal elements of sustainable …