The addition of organic phosphorus-containing flame retardants (OPFRs) has greatly
improved the fire resistance of epoxy resins (EPs). Developing the relationship of the fire …

Carbon-based fillers/polymer nanocomposites for electromagnetic interference (EMI)
shielding have attracted researchers' attention due to their excellent electrical conductivity …

This study proposed an approach utilizing machine learning (ML) to accelerate the design of
organic flame retardants (FRs) for epoxy resins (EPs), avoiding the limitations of traditional …

The present study introduces an artificial neural network (ANN) model coupled with the non-
dominated sorting genetic algorithm (NSGA-II) to establish an advanced model for fatigue …

Machine learning (ML), material genome, and big data approaches are highly overlapped in
their strategies, algorithms, and models. They can target various definitions, distributions …

Calibration is an important step in the development of predictive numerical models that
involves adjusting input parameters not easily measured in experiments to improve the …

In this work, the fire behavior of a sol-gel in-situ hybrid Mg (OH) 2-epoxy nanocomposite was
investigated and an artificial neural network-based system built on a fully connected feed …

Improving the fireproof performance of polymers is crucial for ensuring human safety and
enabling future space colonization. However, the complexity of the mechanisms for flame …

With recent fires in ancient buildings such as Notre-Dame de Paris in France, Wanan
Ancient Bridge in China, and the Cavallerizza Reale in Italy, it can be seen that aging wood …

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 …