Researchers have been consistently facing challenges in achieving the most effective synthesis for maximising flame retardant efficiency. Factors such as mass ratios, molar contributions, and other properties significantly influence the efficiency of flame retardants. To understand the impact of these factors on the effectiveness of flame retardant products, researchers have traditionally relied on costly and time-consuming experimental studies. However, these experimental approaches may not always lead to optimal designs. In recent years, the use of artificial intelligence (AI) has emerged as a promising alternative for investigating the variables that affect the efficiency of fire-retardant polymers. AI-based machine learning algorithms have shown high accuracy in this area. Despite this progress, the design of fire-retardant polymers using machine learning is still in its early stages, and there is a need for more research activities in this area. Phosphorus-based flame retardants (FRs) are commonly used to inhibit the combustion of epoxy (EP) matrices by capturing free radicals and promoting charring. Their effectiveness can be enhanced through combinations and synergistic effects, such as the combination of phosphorus with other flame-retardant elements or groups, as well as the combination of phosphorus-containing FRs with nanomaterials.
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